R Notebook
Run download_data.Rmd and percentage_of_regional_richness.Rmd First!
fetch_city_data_for <- function(pool_name, include_city_name = F) {
results_filename <- paste(paste(pool_name, 'city', 'richness', 'intercept', sep = "_"), "csv", sep = ".")
results <- read_csv(results_filename)
joined <- left_join(city_data, results)
pool_size_col_name <- paste(pool_name, 'pool', 'size', sep = "_")
required_columns <- c("response", pool_size_col_name, "population_growth", "rainfall_monthly_min", "rainfall_annual_average", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "temperature_monthly_max", "happiness_negative_effect", "happiness_positive_effect", "happiness_future_life", "number_of_biomes", "realm", "biome_name", "region_20km_includes_estuary", "region_50km_includes_estuary", "region_100km_includes_estuary", "city_includes_estuary", "region_20km_average_pop_density", "region_50km_average_pop_density", "region_100km_average_pop_density", "city_max_pop_density", "city_average_pop_density", "mean_population_exposure_to_pm2_5_2019", "region_20km_cultivated", "region_20km_urban", "region_50km_cultivated", "region_50km_urban", "region_100km_cultivated", "region_100km_urban", "region_20km_elevation_delta", "region_20km_mean_elevation", "region_50km_elevation_delta", "region_50km_mean_elevation", "region_100km_elevation_delta", "region_100km_mean_elevation", "city_elevation_delta", "city_mean_elevation", "urban", "shrubs", "permanent_water", "open_forest", "herbaceous_wetland", "herbaceous_vegetation", "cultivated", "closed_forest", "share_of_population_within_400m_of_open_space", "percentage_urban_area_as_streets", "percentage_urban_area_as_open_public_spaces_and_streets", "percentage_urban_area_as_open_public_spaces", "city_gdp_per_population")
if (include_city_name) {
required_columns <- append(c("name"), required_columns)
}
joined[,required_columns]
}merlin_city_data <- fetch_city_data_for('merlin')
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
name = col_character(),
response = col_double()
)
Joining, by = "name"merlin_city_datalibrary(randomForest)
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.library(reshape2)
library(rpart)
library(ggplot2)
Attaching package: ‘ggplot2’
The following object is masked from ‘package:randomForest’:
marginlibrary(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ tibble 3.1.2 ✓ dplyr 1.0.7
✓ tidyr 1.1.3 ✓ stringr 1.4.0
✓ readr 1.4.0 ✓ forcats 0.5.1
✓ purrr 0.3.4
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::combine() masks randomForest::combine()
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()
x ggplot2::margin() masks randomForest::margin()merlin_city_data_fixed <- rfImpute(response ~ ., merlin_city_data)
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 3.71 20.58 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 3.979 22.07 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 4.115 22.83 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 4.001 22.19 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 4.161 23.08 |merlin_city_data_fixed <- rfImpute(response ~ ., merlin_city_data) | Out-of-bag |
Tree | MSE %Var(y) |
300 | 3.902 21.65 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 3.999 22.19 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 3.921 21.75 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 3.971 22.03 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 4.177 23.17 |merlin_city_data_fixedrandomForest(response ~ ., merlin_city_data_fixed)
Call:
randomForest(formula = response ~ ., data = merlin_city_data_fixed)
Type of random forest: regression
Number of trees: 500
No. of variables tried at each split: 17
Mean of squared residuals: 16.40603
% Var explained: 8.99select_variables_from_random_forest(merlin_city_data_fixed)
[1] "merlin_pool_size" "realm" "biome_name"
[4] "rainfall_monthly_min" "temperature_annual_average" "happiness_positive_effect"
[7] "region_20km_elevation_delta" "percentage_urban_area_as_open_public_spaces" "region_20km_urban"
[10] "region_50km_elevation_delta" "temperature_monthly_min" "region_20km_cultivated"
[13] "permanent_water" "region_50km_urban" "region_100km_cultivated"
[16] "shrubs" "city_gdp_per_population" "region_50km_cultivated"
[19] "region_100km_elevation_delta" "happiness_negative_effect" "region_100km_urban"
[22] "region_50km_average_pop_density" "region_20km_average_pop_density" "share_of_population_within_400m_of_open_space"
[25] "rainfall_annual_average" "city_average_pop_density" "herbaceous_wetland"
[28] "temperature_monthly_max" "region_100km_average_pop_density" "city_mean_elevation"
[31] "mean_population_exposure_to_pm2_5_2019" "rainfall_monthly_max" "happiness_future_life"
[34] "city_max_pop_density" "region_50km_mean_elevation" "cultivated"
[37] "region_20km_mean_elevation" "region_100km_mean_elevation" "urban"
[40] "population_growth" "percentage_urban_area_as_open_public_spaces_and_streets" "open_forest"
[43] "percentage_urban_area_as_streets" "closed_forest" select_variables_from_random_forest(merlin_city_data_fixed_single_scale)
[1] "merlin_pool_size" "realm" "biome_name"
[4] "temperature_annual_average" "happiness_positive_effect" "region_20km_elevation_delta"
[7] "percentage_urban_area_as_open_public_spaces" "rainfall_monthly_min" "permanent_water"
[10] "temperature_monthly_min" "region_20km_urban" "shrubs"
[13] "region_20km_cultivated" "happiness_negative_effect" "share_of_population_within_400m_of_open_space"
[16] "temperature_monthly_max" "rainfall_monthly_max" "rainfall_annual_average"
[19] "happiness_future_life" "city_max_pop_density" "city_mean_elevation"
[22] "city_elevation_delta" "cultivated" "population_growth"
[25] "region_50km_mean_elevation" "percentage_urban_area_as_streets" "open_forest"
[28] "closed_forest" create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size")])
[1] "Mean 18.3605779376349 , SD: 0.199971871406664 , Mean + SD: 18.5605498090416"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm")])[1] "Mean 13.8483857782536 , SD: 0.167233775821336 , Mean + SD: 14.015619554075"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name")])[1] "Mean 14.1417551442684 , SD: 0.160688673425095 , Mean + SD: 14.3024438176934"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average")])[1] "Mean 14.5103292900374 , SD: 0.234962153093279 , Mean + SD: 14.7452914431307"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect")])[1] "Mean 14.7574657636192 , SD: 0.243199251750122 , Mean + SD: 15.0006650153693"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta")])[1] "Mean 14.9256981639764 , SD: 0.240704529916054 , Mean + SD: 15.1664026938924"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces")])[1] "Mean 14.788101572417 , SD: 0.263257912139604 , Mean + SD: 15.0513594845566"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min")])[1] "Mean 14.708125023514 , SD: 0.244893814930232 , Mean + SD: 14.9530188384442"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water")])[1] "Mean 14.71026136053 , SD: 0.251430414275647 , Mean + SD: 14.9616917748057"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min")])[1] "Mean 15.0471745956683 , SD: 0.215420444110583 , Mean + SD: 15.2625950397789"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban")])[1] "Mean 15.1350022423199 , SD: 0.339891929354689 , Mean + SD: 15.4748941716745"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs")])[1] "Mean 15.1353707728019 , SD: 0.319439216090642 , Mean + SD: 15.4548099888925"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated")])[1] "Mean 15.169974474564 , SD: 0.270058572377652 , Mean + SD: 15.4400330469417"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect")])[1] "Mean 15.2117893322605 , SD: 0.300179792841188 , Mean + SD: 15.5119691251017"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space")])[1] "Mean 15.2800537211106 , SD: 0.287378331903091 , Mean + SD: 15.5674320530137"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max")])[1] "Mean 15.4080077907785 , SD: 0.299436746178816 , Mean + SD: 15.7074445369573"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max", "rainfall_monthly_max")])[1] "Mean 15.6416908542101 , SD: 0.24366735672925 , Mean + SD: 15.8853582109394"create_fifty_rows_of_oob(merlin_city_data_fixed[,c("response", "merlin_pool_size", "realm", "biome_name", "temperature_annual_average", "happiness_positive_effect", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces", "rainfall_monthly_min", "permanent_water", "temperature_monthly_min", "region_20km_urban", "shrubs", "region_20km_cultivated", "happiness_negative_effect", "share_of_population_within_400m_of_open_space", "temperature_monthly_max", "rainfall_monthly_max", "rainfall_annual_average")])[1] "Mean 15.6107006616448 , SD: 0.240063905776268 , Mean + SD: 15.850764567421"“merlin_pool_size”, “realm”
birdlife_city_data <- fetch_city_data_for('birdlife')
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
name = col_character(),
response = col_double()
)
Joining, by = "name"birdlife_city_data <- fetch_city_data_for('birdlife')
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
name = col_character(),
response = col_double()
)
Joining, by = "name"birdlife_city_databirdlife_city_data_fixed <- rfImpute(response ~ ., birdlife_city_data)
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 5.546 87.80 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 5.56 88.01 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 5.312 84.08 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 5.467 86.55 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 5.482 86.77 |birdlife_city_data_fixedselect_variables_from_random_forest(birdlife_city_data_fixed)
[1] "population_growth" "birdlife_pool_size" "region_100km_cultivated"
[4] "biome_name" "region_20km_average_pop_density" "rainfall_monthly_min"
[7] "percentage_urban_area_as_open_public_spaces" "region_50km_cultivated" "permanent_water"
[10] "region_50km_average_pop_density" "rainfall_monthly_max" "mean_population_exposure_to_pm2_5_2019"
[13] "temperature_monthly_min" "shrubs" "temperature_annual_average"
[16] "region_100km_average_pop_density" "region_100km_urban" "region_20km_cultivated"
[19] "percentage_urban_area_as_open_public_spaces_and_streets" "region_20km_elevation_delta" "share_of_population_within_400m_of_open_space"
[22] "region_20km_urban" "city_average_pop_density" "happiness_future_life"
[25] "region_50km_elevation_delta" "region_50km_urban" "open_forest"
[28] "percentage_urban_area_as_streets" "temperature_monthly_max" "realm"
[31] "city_max_pop_density" "city_elevation_delta" "rainfall_annual_average"
[34] "city_gdp_per_population" "cultivated" "happiness_negative_effect"
[37] "region_100km_mean_elevation" "region_50km_mean_elevation" "city_mean_elevation"
[40] "closed_forest" "happiness_positive_effect" "herbaceous_wetland"
[43] "urban" "herbaceous_vegetation" select_variables_from_random_forest(birdlife_city_data_fixed_single_scale)
[1] "population_growth" "birdlife_pool_size" "region_100km_cultivated"
[4] "percentage_urban_area_as_open_public_spaces" "biome_name" "rainfall_monthly_min"
[7] "region_20km_average_pop_density" "permanent_water" "rainfall_monthly_max"
[10] "temperature_annual_average" "temperature_monthly_min" "mean_population_exposure_to_pm2_5_2019"
[13] "region_100km_urban" "shrubs" "region_20km_elevation_delta"
[16] "percentage_urban_area_as_open_public_spaces_and_streets" "share_of_population_within_400m_of_open_space" "realm"
[19] "city_average_pop_density" "open_forest" "happiness_future_life"
[22] "city_elevation_delta" "temperature_monthly_max" "rainfall_annual_average"
[25] "percentage_urban_area_as_streets" "city_gdp_per_population" "cultivated"
[28] "happiness_negative_effect" "closed_forest" "region_50km_mean_elevation"
[31] "city_mean_elevation" "herbaceous_wetland" "urban"
[34] "herbaceous_vegetation" create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth")])
[1] "Mean 6.35474438479356 , SD: 0.0712540254137255 , Mean + SD: 6.42599841020729"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size")])[1] "Mean 5.53431593845832 , SD: 0.0808938134022798 , Mean + SD: 5.6152097518606"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated")])[1] "Mean 5.03645460544453 , SD: 0.080993921046237 , Mean + SD: 5.11744852649077"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name")])[1] "Mean 5.01181482503518 , SD: 0.0829095109876754 , Mean + SD: 5.09472433602285"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min")])[1] "Mean 4.97549471316348 , SD: 0.0685833826522881 , Mean + SD: 5.04407809581577"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density")])[1] "Mean 4.86486157758304 , SD: 0.0978544677155927 , Mean + SD: 4.96271604529863"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water")])[1] "Mean 4.75503603367959 , SD: 0.0820218992111515 , Mean + SD: 4.83705793289075"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max")])[1] "Mean 4.83291787856041 , SD: 0.0877474150947955 , Mean + SD: 4.9206652936552"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average")])[1] "Mean 4.89459108748752 , SD: 0.0675800594452371 , Mean + SD: 4.96217114693276"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min")])[1] "Mean 4.87244265396031 , SD: 0.0892281563604926 , Mean + SD: 4.9616708103208"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019")])[1] "Mean 4.83199994339927 , SD: 0.0822602681223139 , Mean + SD: 4.91426021152158"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban")])[1] "Mean 4.82180678041379 , SD: 0.0733987377154572 , Mean + SD: 4.89520551812925"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs")])[1] "Mean 4.88760637080854 , SD: 0.0708285469155194 , Mean + SD: 4.95843491772406"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta")])[1] "Mean 4.92024268589129 , SD: 0.102386290093399 , Mean + SD: 5.02262897598469"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets")])[1] "Mean 4.91143919034019 , SD: 0.0837468464163517 , Mean + SD: 4.99518603675654"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space")])[1] "Mean 4.96416771734588 , SD: 0.0825668458432887 , Mean + SD: 5.04673456318917"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm")])[1] "Mean 4.98107377749941 , SD: 0.0814576329581648 , Mean + SD: 5.06253141045758"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm", "city_average_pop_density")])[1] "Mean 4.9987069406936 , SD: 0.0742949984686252 , Mean + SD: 5.07300193916222"create_fifty_rows_of_oob(birdlife_city_data_fixed[,c("response", "population_growth", "birdlife_pool_size", "region_100km_cultivated", "percentage_urban_area_as_open_public_spaces", "biome_name", "rainfall_monthly_min", "region_20km_average_pop_density", "permanent_water", "rainfall_monthly_max", "temperature_annual_average", "temperature_monthly_min", "mean_population_exposure_to_pm2_5_2019", "region_100km_urban", "shrubs", "region_20km_elevation_delta", "percentage_urban_area_as_open_public_spaces_and_streets", "share_of_population_within_400m_of_open_space", "realm", "city_average_pop_density", "open_forest", "happiness_future_life")])[1] "Mean 5.02149734174997 , SD: 0.0854499582879466 , Mean + SD: 5.10694730003792"“population_growth”, “birdlife_pool_size”, “region_100km_cultivated”, “percentage_urban_area_as_open_public_spaces”, “biome_name”, “rainfall_monthly_min”, “region_20km_average_pop_density”, “permanent_water”
either_city_data <- fetch_city_data_for('either')
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
name = col_character(),
response = col_double()
)
Joining, by = "name"either_city_dataeither_city_data_fixed <- rfImpute(response ~ ., either_city_data)
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 4.824 94.87 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 4.613 90.71 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 4.655 91.54 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 4.605 90.56 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 4.581 90.08 |either_city_data_fixedselect_variables_from_random_forest(either_city_data_fixed)
[1] "either_pool_size" "population_growth" "region_100km_cultivated"
[4] "region_20km_average_pop_density" "realm" "region_50km_cultivated"
[7] "region_50km_average_pop_density" "biome_name" "shrubs"
[10] "rainfall_monthly_min" "region_100km_average_pop_density" "permanent_water"
[13] "region_20km_cultivated" "temperature_monthly_min" "region_50km_elevation_delta"
[16] "region_20km_urban" "mean_population_exposure_to_pm2_5_2019" "region_20km_elevation_delta"
[19] "percentage_urban_area_as_open_public_spaces" "city_average_pop_density" "happiness_future_life"
[22] "rainfall_monthly_max" "temperature_annual_average" "temperature_monthly_max"
[25] "region_100km_urban" "region_50km_urban" "cultivated"
[28] "share_of_population_within_400m_of_open_space" "city_max_pop_density" "city_elevation_delta"
[31] "city_mean_elevation" "herbaceous_wetland" "rainfall_annual_average"
[34] "region_100km_elevation_delta" "city_gdp_per_population" "region_20km_mean_elevation"
[37] "percentage_urban_area_as_open_public_spaces_and_streets" "region_50km_mean_elevation" "region_100km_mean_elevation"
[40] "happiness_negative_effect" "open_forest" "urban"
[43] "happiness_positive_effect" "herbaceous_vegetation" "percentage_urban_area_as_streets"
[46] "closed_forest" select_variables_from_random_forest(either_city_data_fixed_single_scale)
[1] "either_pool_size" "population_growth" "region_100km_cultivated"
[4] "region_20km_average_pop_density" "realm" "biome_name"
[7] "rainfall_monthly_min" "shrubs" "temperature_monthly_min"
[10] "permanent_water" "percentage_urban_area_as_open_public_spaces" "region_20km_urban"
[13] "region_50km_elevation_delta" "mean_population_exposure_to_pm2_5_2019" "city_average_pop_density"
[16] "rainfall_monthly_max" "happiness_future_life" "cultivated"
[19] "share_of_population_within_400m_of_open_space" "city_elevation_delta" "city_max_pop_density"
[22] "rainfall_annual_average" "city_mean_elevation" "percentage_urban_area_as_open_public_spaces_and_streets"
[25] "temperature_monthly_max" "region_20km_mean_elevation" "open_forest"
[28] "happiness_negative_effect" "urban" "percentage_urban_area_as_streets"
[31] "closed_forest" create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size")])
[1] "Mean 4.69133292981281 , SD: 0.0489285631824466 , Mean + SD: 4.74026149299525"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth")])[1] "Mean 4.18668186942246 , SD: 0.0652412455486544 , Mean + SD: 4.25192311497111"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated")])[1] "Mean 4.08678736410676 , SD: 0.05782944992901 , Mean + SD: 4.14461681403577"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density")])[1] "Mean 3.7612122389608 , SD: 0.0626910363388465 , Mean + SD: 3.82390327529965"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm")])[1] "Mean 3.62757829197622 , SD: 0.0641414031735027 , Mean + SD: 3.69171969514973"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name")])[1] "Mean 3.88541888033384 , SD: 0.0697761912903623 , Mean + SD: 3.9551950716242"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min")])[1] "Mean 3.94442754211899 , SD: 0.0705357146629719 , Mean + SD: 4.01496325678196"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs")])[1] "Mean 3.94268869246952 , SD: 0.0683286721896993 , Mean + SD: 4.01101736465922"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min")])[1] "Mean 3.91638012322931 , SD: 0.058327290718483 , Mean + SD: 3.97470741394779"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water")])[1] "Mean 3.97807710896339 , SD: 0.0819372375299245 , Mean + SD: 4.06001434649332"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces")])[1] "Mean 4.08096128871886 , SD: 0.0742590817487434 , Mean + SD: 4.15522037046761"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban")])[1] "Mean 4.10276372102361 , SD: 0.0793013544355574 , Mean + SD: 4.18206507545916"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta")])[1] "Mean 4.14360043109383 , SD: 0.0571122481729091 , Mean + SD: 4.20071267926674"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019")])[1] "Mean 4.17420426151807 , SD: 0.0800438347270345 , Mean + SD: 4.25424809624511"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density")])[1] "Mean 4.20142417235852 , SD: 0.0534400086677398 , Mean + SD: 4.25486418102626"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max")])[1] "Mean 4.23495161199434 , SD: 0.0810189022929709 , Mean + SD: 4.31597051428731"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life")])[1] "Mean 4.25511343099509 , SD: 0.0777504142418531 , Mean + SD: 4.33286384523695"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated")])[1] "Mean 4.27187737013208 , SD: 0.0659717347812921 , Mean + SD: 4.33784910491338"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space")])[1] "Mean 4.32785556380064 , SD: 0.0915334527831198 , Mean + SD: 4.41938901658376"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space", "city_elevation_delta")])[1] "Mean 4.33688765161498 , SD: 0.0687985208297185 , Mean + SD: 4.4056861724447"create_fifty_rows_of_oob(either_city_data_fixed[,c("response", "either_pool_size", "population_growth", "region_100km_cultivated", "region_20km_average_pop_density", "realm", "biome_name", "rainfall_monthly_min", "shrubs", "temperature_monthly_min", "permanent_water", "percentage_urban_area_as_open_public_spaces", "region_20km_urban", "region_50km_elevation_delta", "mean_population_exposure_to_pm2_5_2019", "city_average_pop_density", "rainfall_monthly_max", "happiness_future_life", "cultivated", "share_of_population_within_400m_of_open_space", "city_elevation_delta", "city_max_pop_density")])[1] "Mean 4.35933973615371 , SD: 0.0853454117772923 , Mean + SD: 4.444685147931"“either_pool_size”, “population_growth”, “region_100km_cultivated”, “region_20km_average_pop_density”, “realm”
both_city_data <- fetch_city_data_for('both')
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
name = col_character(),
response = col_double()
)
Joining, by = "name"both_city_databoth_city_data_fixed <- rfImpute(response ~ ., both_city_data)
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 16.32 99.56 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 15.46 94.36 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 15.88 96.87 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 15.65 95.52 |
| Out-of-bag |
Tree | MSE %Var(y) |
300 | 15.91 97.08 |both_city_data_fixedselect_variables_from_random_forest(both_city_data_fixed)
[1] "both_pool_size" "temperature_annual_average" "temperature_monthly_min"
[4] "permanent_water" "happiness_negative_effect" "region_20km_urban"
[7] "region_100km_cultivated" "region_50km_cultivated" "realm"
[10] "region_20km_cultivated" "rainfall_monthly_min" "region_50km_elevation_delta"
[13] "population_growth" "shrubs" "region_100km_elevation_delta"
[16] "region_20km_average_pop_density" "region_100km_urban" "biome_name"
[19] "region_20km_elevation_delta" "region_50km_urban" "percentage_urban_area_as_open_public_spaces"
[22] "city_average_pop_density" "city_gdp_per_population" "region_50km_average_pop_density"
[25] "open_forest" "herbaceous_wetland" "cultivated"
[28] "region_100km_average_pop_density" "region_20km_mean_elevation" "share_of_population_within_400m_of_open_space"
[31] "mean_population_exposure_to_pm2_5_2019" "city_elevation_delta" "region_50km_mean_elevation"
[34] "happiness_future_life" "happiness_positive_effect" "rainfall_monthly_max"
[37] "percentage_urban_area_as_open_public_spaces_and_streets" "herbaceous_vegetation" "temperature_monthly_max"
[40] "percentage_urban_area_as_streets" "rainfall_annual_average" "urban"
[43] "closed_forest" select_variables_from_random_forest(both_city_data_fixed_single_scale)
[1] "both_pool_size" "temperature_annual_average" "temperature_monthly_min"
[4] "permanent_water" "happiness_negative_effect" "region_20km_urban"
[7] "rainfall_monthly_min" "realm" "region_100km_cultivated"
[10] "region_50km_elevation_delta" "population_growth" "percentage_urban_area_as_open_public_spaces"
[13] "shrubs" "biome_name" "region_20km_average_pop_density"
[16] "city_mean_elevation" "city_gdp_per_population" "share_of_population_within_400m_of_open_space"
[19] "cultivated" "open_forest" "region_20km_mean_elevation"
[22] "rainfall_monthly_max" "temperature_monthly_max" "rainfall_annual_average"
[25] "percentage_urban_area_as_streets" "closed_forest" "urban" create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size")])
[1] "Mean 17.0501746482574 , SD: 0.180832956779973 , Mean + SD: 17.2310076050374"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average")])[1] "Mean 14.1770117377572 , SD: 0.12741540946317 , Mean + SD: 14.3044271472204"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min")])[1] "Mean 13.9947226485825 , SD: 0.193418308082352 , Mean + SD: 14.1881409566648"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water")])[1] "Mean 13.980419606819 , SD: 0.198277385993035 , Mean + SD: 14.1786969928121"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect")])[1] "Mean 14.250869948595 , SD: 0.205833621996072 , Mean + SD: 14.4567035705911"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban")])[1] "Mean 13.831253031623 , SD: 0.267164827147086 , Mean + SD: 14.0984178587701"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min")])[1] "Mean 14.0176076470363 , SD: 0.233933915226803 , Mean + SD: 14.2515415622631"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm")])[1] "Mean 13.9678429656754 , SD: 0.27500268619645 , Mean + SD: 14.2428456518719"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated"),])[1] "Mean 13.8133557218721 , SD: 0.208047207294211 , Mean + SD: 14.0214029291663"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta"),])[1] "Mean 14.1587620560898 , SD: 0.265407381711604 , Mean + SD: 14.4241694378014"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth"),])[1] "Mean 14.3695231260649 , SD: 0.277971868419252 , Mean + SD: 14.6474949944841"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces"),])[1] "Mean 14.6989193629512 , SD: 0.281371086172517 , Mean + SD: 14.9802904491237"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs"),])[1] "Mean 14.6255340760297 , SD: 0.287689760117654 , Mean + SD: 14.9132238361473"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name"),])[1] "Mean 14.6561287164134 , SD: 0.265987324658439 , Mean + SD: 14.9221160410718"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density"),])[1] "Mean 14.7323907720158 , SD: 0.194408848363496 , Mean + SD: 14.9267996203793"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation"),])[1] "Mean 14.7915916869301 , SD: 0.298364380477512 , Mean + SD: 15.0899560674076"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation", "city_gdp_per_population"),])[1] "Mean 14.9134394356877 , SD: 0.224050531561373 , Mean + SD: 15.137489967249"create_fifty_rows_of_oob(both_city_data_fixed[,c("response", "both_pool_size", "temperature_annual_average", "temperature_monthly_min", "permanent_water", "happiness_negative_effect", "region_20km_urban", "rainfall_monthly_min", "realm", "region_100km_cultivated", "region_50km_elevation_delta", "population_growth", "percentage_urban_area_as_open_public_spaces", "shrubs", "biome_name", "region_20km_average_pop_density", "city_mean_elevation", "city_gdp_per_population", "share_of_population_within_400m_of_open_space"),])[1] "Mean 15.0099497484692 , SD: 0.2218461976947 , Mean + SD: 15.2317959461639"“both_pool_size”, “temperature_annual_average”, “happiness_negative_effect”
| So…. |
|---|
| “merlin_pool_size”, “realm” “population_growth”, “birdlife_pool_size”, “region_100km_cultivated”, “percentage_urban_area_as_open_public_spaces”, “biome_name”, “rainfall_monthly_min”, “region_20km_average_pop_density”, “permanent_water” “either_pool_size”, “population_growth”, “region_100km_cultivated”, “region_20km_average_pop_density”, “realm” “both_pool_size”, “temperature_annual_average”, “temperature_monthly_min” |
| ```r summary(lm(response ~ merlin_pool_size, merlin_city_data_fixed)) |
| ``` |
| ``` |
| Call: lm(formula = response ~ merlin_pool_size, data = merlin_city_data_fixed) |
| Residuals: Min 1Q Median 3Q Max -8.3644 -2.2493 -0.3649 1.7804 15.4604 |
| Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.205975 0.920945 6.739 4.23e-10 merlin_pool_size -0.022439 0.003134 -7.160 4.71e-11 |
Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.641 on 135 degrees of freedom Multiple R-squared: 0.2752, Adjusted R-squared: 0.2698 F-statistic: 51.26 on 1 and 135 DF, p-value: 4.707e-11
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```r
summary(lm(response ~ birdlife_pool_size, birdlife_city_data_fixed))
Call:
lm(formula = response ~ birdlife_pool_size, data = birdlife_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-5.140 -1.330 -0.313 1.034 9.156
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.602931 0.625873 4.159 5.65e-05 ***
birdlife_pool_size -0.008789 0.002000 -4.395 2.23e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.368 on 135 degrees of freedom
Multiple R-squared: 0.1252, Adjusted R-squared: 0.1187
F-statistic: 19.31 on 1 and 135 DF, p-value: 2.225e-05summary(lm(response ~ either_pool_size, either_city_data_fixed))
Call:
lm(formula = response ~ either_pool_size, data = either_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-4.8488 -1.0658 -0.3811 0.8665 6.5921
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.250304 0.584389 5.562 1.38e-07 ***
either_pool_size -0.009005 0.001546 -5.825 3.99e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.031 on 135 degrees of freedom
Multiple R-squared: 0.2008, Adjusted R-squared: 0.1949
F-statistic: 33.92 on 1 and 135 DF, p-value: 3.99e-08summary(lm(response ~ both_pool_size, both_city_data_fixed))
Call:
lm(formula = response ~ both_pool_size, data = both_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-8.9674 -2.7370 -0.3475 1.8439 10.3398
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.261657 0.982371 5.356 3.56e-07 ***
both_pool_size -0.024842 0.004396 -5.651 9.08e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.667 on 135 degrees of freedom
Multiple R-squared: 0.1913, Adjusted R-squared: 0.1853
F-statistic: 31.94 on 1 and 135 DF, p-value: 9.076e-08
summary(lm(response ~ region_100km_cultivated, merlin_city_data_fixed))
Call:
lm(formula = response ~ region_100km_cultivated, data = merlin_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-8.7405 -2.8276 -0.5911 1.5098 18.0590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6281 0.5172 -1.214 0.2267
region_100km_cultivated 2.3444 1.3805 1.698 0.0918 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.232 on 135 degrees of freedom
Multiple R-squared: 0.02092, Adjusted R-squared: 0.01366
F-statistic: 2.884 on 1 and 135 DF, p-value: 0.09176summary(lm(response ~ region_100km_cultivated, birdlife_city_data_fixed))
Call:
lm(formula = response ~ region_100km_cultivated, data = birdlife_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-4.4506 -1.5884 -0.3702 1.3865 9.9581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.6226 0.3002 -2.074 0.04001 *
region_100km_cultivated 2.3237 0.8013 2.900 0.00436 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.457 on 135 degrees of freedom
Multiple R-squared: 0.05864, Adjusted R-squared: 0.05167
F-statistic: 8.409 on 1 and 135 DF, p-value: 0.004359summary(lm(response ~ region_100km_cultivated, either_city_data_fixed))
Call:
lm(formula = response ~ region_100km_cultivated, data = either_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-4.6522 -1.4255 -0.2114 0.9771 6.3724
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.5459 0.2698 -2.024 0.04499 *
region_100km_cultivated 2.0373 0.7200 2.830 0.00537 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.207 on 135 degrees of freedom
Multiple R-squared: 0.05599, Adjusted R-squared: 0.049
F-statistic: 8.008 on 1 and 135 DF, p-value: 0.00537summary(lm(response ~ region_100km_cultivated, both_city_data_fixed))
Call:
lm(formula = response ~ region_100km_cultivated, data = both_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-8.439 -2.791 -0.689 1.898 12.088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.7221 0.4908 -1.471 0.1436
region_100km_cultivated 2.6951 1.3099 2.057 0.0416 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.016 on 135 degrees of freedom
Multiple R-squared: 0.0304, Adjusted R-squared: 0.02322
F-statistic: 4.233 on 1 and 135 DF, p-value: 0.04157ggplot() +
geom_point(aes(x = merlin_pool_size, y = region_100km_cultivated), merlin_city_data_fixed, color = "red") +
geom_point(aes(x = birdlife_pool_size, y = region_100km_cultivated), birdlife_city_data_fixed, color = "blue") +
geom_point(aes(x = either_pool_size, y = region_100km_cultivated), either_city_data_fixed, color = "green") +
geom_point(aes(x = both_pool_size, y = region_100km_cultivated), both_city_data_fixed, color = "purple")
ggplot() +
geom_point(aes(x = population_growth, y = response), merlin_city_data_fixed, color = "red") +
geom_point(aes(x = population_growth, y = response), birdlife_city_data_fixed, color = "blue") +
geom_point(aes(x = population_growth, y = response), either_city_data_fixed, color = "green") +
geom_point(aes(x = population_growth, y = response), both_city_data_fixed, color = "purple")
ggplot() +
geom_point(aes(x = merlin_pool_size, y = region_20km_average_pop_density), merlin_city_data_fixed, color = "red") +
geom_point(aes(x = birdlife_pool_size, y = region_20km_average_pop_density), birdlife_city_data_fixed, color = "blue") +
geom_point(aes(x = either_pool_size, y = region_20km_average_pop_density), either_city_data_fixed, color = "green") +
geom_point(aes(x = both_pool_size, y = region_20km_average_pop_density), both_city_data_fixed, color = "purple")
summary(lm(response ~ population_growth, merlin_city_data_fixed))
Call:
lm(formula = response ~ population_growth, data = merlin_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-9.2751 -2.8391 -0.4272 1.4837 18.4058
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.094091 0.524516 0.179 0.858
population_growth -0.001479 0.005915 -0.250 0.803
Residual standard error: 4.276 on 135 degrees of freedom
Multiple R-squared: 0.0004627, Adjusted R-squared: -0.006941
F-statistic: 0.0625 on 1 and 135 DF, p-value: 0.803summary(lm(response ~ population_growth, birdlife_city_data_fixed))
Call:
lm(formula = response ~ population_growth, data = birdlife_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-5.085 -1.538 -0.459 1.240 10.226
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.231365 0.309332 0.748 0.456
population_growth -0.003636 0.003489 -1.042 0.299
Residual standard error: 2.522 on 135 degrees of freedom
Multiple R-squared: 0.007984, Adjusted R-squared: 0.0006359
F-statistic: 1.087 on 1 and 135 DF, p-value: 0.2991summary(lm(response ~ population_growth, either_city_data_fixed))
Call:
lm(formula = response ~ population_growth, data = either_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-5.1409 -1.3284 -0.1829 0.8324 6.7919
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.113195 0.278318 0.407 0.685
population_growth -0.001779 0.003139 -0.567 0.572
Residual standard error: 2.269 on 135 degrees of freedom
Multiple R-squared: 0.002374, Adjusted R-squared: -0.005016
F-statistic: 0.3213 on 1 and 135 DF, p-value: 0.5718summary(lm(response ~ population_growth, both_city_data_fixed))
Call:
lm(formula = response ~ population_growth, data = both_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-9.1143 -2.5568 -0.7818 2.1289 12.4621
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.188410 0.499736 0.377 0.707
population_growth -0.002961 0.005636 -0.525 0.600
Residual standard error: 4.074 on 135 degrees of freedom
Multiple R-squared: 0.002041, Adjusted R-squared: -0.005351
F-statistic: 0.2761 on 1 and 135 DF, p-value: 0.6001summary(lm(response ~ rainfall_monthly_min, merlin_city_data_fixed))
Call:
lm(formula = response ~ rainfall_monthly_min, data = merlin_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-9.2835 -2.9452 -0.4893 1.4983 18.2505
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.191459 0.491332 0.390 0.697
rainfall_monthly_min -0.007481 0.012853 -0.582 0.562
Residual standard error: 4.272 on 135 degrees of freedom
Multiple R-squared: 0.002503, Adjusted R-squared: -0.004886
F-statistic: 0.3387 on 1 and 135 DF, p-value: 0.5615summary(lm(response ~ rainfall_monthly_min, birdlife_city_data_fixed))
Call:
lm(formula = response ~ rainfall_monthly_min, data = birdlife_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-5.0114 -1.4084 -0.4231 1.3632 10.6767
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.244199 0.289526 0.843 0.40
rainfall_monthly_min -0.009541 0.007574 -1.260 0.21
Residual standard error: 2.517 on 135 degrees of freedom
Multiple R-squared: 0.01162, Adjusted R-squared: 0.004298
F-statistic: 1.587 on 1 and 135 DF, p-value: 0.2099summary(lm(response ~ rainfall_monthly_min, either_city_data_fixed))
Call:
lm(formula = response ~ rainfall_monthly_min, data = either_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-5.1121 -1.3720 -0.2964 0.8111 6.5298
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.219743 0.259756 0.846 0.399
rainfall_monthly_min -0.008586 0.006795 -1.264 0.209
Residual standard error: 2.258 on 135 degrees of freedom
Multiple R-squared: 0.01169, Adjusted R-squared: 0.004367
F-statistic: 1.597 on 1 and 135 DF, p-value: 0.2086summary(lm(response ~ rainfall_monthly_min, both_city_data_fixed))
Call:
lm(formula = response ~ rainfall_monthly_min, data = both_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-9.0991 -2.8506 -0.8491 1.9009 12.2257
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.30602 0.46742 0.655 0.514
rainfall_monthly_min -0.01196 0.01223 -0.978 0.330
Residual standard error: 4.064 on 135 degrees of freedom
Multiple R-squared: 0.007033, Adjusted R-squared: -0.0003223
F-statistic: 0.9562 on 1 and 135 DF, p-value: 0.3299ggplot() +
geom_point(aes(x = rainfall_monthly_min, y = response), merlin_city_data_fixed, color = "red") +
geom_point(aes(x = rainfall_monthly_min, y = response), birdlife_city_data_fixed, color = "blue") +
geom_point(aes(x = rainfall_monthly_min, y = response), either_city_data_fixed, color = "green") +
geom_point(aes(x = rainfall_monthly_min, y = response), both_city_data_fixed, color = "purple")
ggplot() +
geom_point(aes(x = temperature_annual_average, y = response), merlin_city_data_fixed, color = "red") +
geom_point(aes(x = temperature_annual_average, y = response), birdlife_city_data_fixed, color = "blue") +
geom_point(aes(x = temperature_annual_average, y = response), either_city_data_fixed, color = "green") +
geom_point(aes(x = temperature_annual_average, y = response), both_city_data_fixed, color = "purple")
ggplot() +
geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), merlin_city_data_fixed, color = "red") +
geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), birdlife_city_data_fixed, color = "blue") +
geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), either_city_data_fixed, color = "green") +
geom_point(aes(x = percentage_urban_area_as_open_public_spaces, y = response), both_city_data_fixed, color = "purple")
ggplot() +
geom_point(aes(x = happiness_negative_effect, y = response), merlin_city_data_fixed, color = "red") +
geom_point(aes(x = happiness_negative_effect, y = response), birdlife_city_data_fixed, color = "blue") +
geom_point(aes(x = happiness_negative_effect, y = response), either_city_data_fixed, color = "green") +
geom_point(aes(x = happiness_negative_effect, y = response), both_city_data_fixed, color = "purple")
ggplot() +
geom_boxplot(aes(x = response, y = biome_name), merlin_city_data_fixed)
ggplot() +
geom_boxplot(aes(x = response, y = biome_name), birdlife_city_data_fixed)
ggplot() +
geom_boxplot(aes(x = response, y = biome_name), either_city_data_fixed)
ggplot() +
geom_boxplot(aes(x = response, y = biome_name), both_city_data_fixed)
summary(lm(response ~ biome_name, merlin_city_data_fixed))
Call:
lm(formula = response ~ biome_name, data = merlin_city_data_fixed)
Residuals:
Min 1Q Median 3Q Max
-8.7663 -2.4594 -0.4676 2.1272 18.4309
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.2599 4.2666 -0.764 0.4463
biome_nameDeserts & Xeric Shrublands 3.1836 4.4563 0.714 0.4763
biome_nameFlooded Grasslands & Savannas 0.6618 5.2255 0.127 0.8994
biome_nameMangroves 9.3150 5.2255 1.783 0.0771 .
biome_nameMediterranean Forests, Woodlands & Scrub 3.2643 4.4066 0.741 0.4602
biome_nameMontane Grasslands & Shrublands 1.5344 5.2255 0.294 0.7695
biome_nameTemperate Broadleaf & Mixed Forests 3.2942 4.3328 0.760 0.4485
biome_nameTemperate Conifer Forests 3.3572 5.2255 0.642 0.5218
biome_nameTemperate Grasslands, Savannas & Shrublands 4.3835 4.6739 0.938 0.3501
biome_nameTropical & Subtropical Coniferous Forests 7.4846 5.2255 1.432 0.1546
biome_nameTropical & Subtropical Dry Broadleaf Forests 3.7631 4.4164 0.852 0.3958
biome_nameTropical & Subtropical Grasslands, Savannas & Shrublands 5.9138 4.6739 1.265 0.2081
biome_nameTropical & Subtropical Moist Broadleaf Forests 2.4622 4.3148 0.571 0.5693
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.267 on 124 degrees of freedom
Multiple R-squared: 0.08597, Adjusted R-squared: -0.002489
F-statistic: 0.9719 on 12 and 124 DF, p-value: 0.4793| In Summary |
Response is related to number of species in regional pool, the more species, the less the percentage of species in the city. Indicating a fixed number of species are able to move into cities. The size of the regional pool is correlated with both the amount of urban and cultivated land cover, both reduce species in the regional pool.
Response is also lower in wet biomes and areas of the world, this is seen through the higher rainfall in the month with least rainfall in the year, and lower percentages in wetter biomes such as flooded grasslands and moist broadleaf forests.
Finally cities with a higher proportion of green public space are less likely to have a low response.
merlin_city_data_2 <- fetch_city_data_for('merlin', T)
birdlife_city_data_2 <- fetch_city_data_for('birdlife', T)merlin_city_data$residuals_pool_size
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
3.6346589 -1.3808445 1.0649349 -3.4355790 -4.3126647 -0.7438113 -7.7336701 -2.4387017 1.4453341 -1.0023679 -3.6997976 -1.3953352 7.7497245 -0.8622274 0.5366795 4.5265798 1.0617137
18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
-0.2523214 4.7814815 -3.1223646 1.7091096 -3.6108085 -0.1397141 15.4603887 -0.6266225 -3.0909248 1.0182028 1.7201615 -2.7031265 8.5064874 8.8776781 7.0569196 0.7062920 -1.2592215
35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
1.2336978 0.1383472 7.0252049 2.2268554 -2.0718664 1.9814449 3.8241566 7.2649549 6.1520103 3.4917821 -0.1258741 1.8033208 0.4220553 -2.0346183 -3.6731730 -0.6081145 -4.7650229
52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68
-0.6374446 5.3381301 -0.5829402 -5.3003161 -1.6870541 3.8366707 4.8978947 0.2824830 -1.4631580 -1.6682595 -3.0684045 1.8602911 2.1229221 -3.4328909 0.7519428 -2.0768581 -1.3890451
69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85
-5.0351403 -2.1185232 -0.7391181 2.2308499 0.1957247 1.8741271 -3.0258162 -3.2283923 -5.4035769 -2.1190007 -5.5415234 -1.3942752 2.2271298 2.4763802 0.9951147 7.2106299 -3.6705115
86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102
-4.0562506 -2.6050825 2.2253612 -0.6344070 -1.7984823 -1.3325552 -2.2520966 6.5027968 -0.4457886 1.0550630 -3.3616225 -4.5509836 0.4535341 0.5300748 -0.9801669 2.9973939 1.3750704
103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119
1.5953003 -4.9345018 -2.4455672 7.0465516 -1.7276856 -0.3215003 -0.4133246 2.9320579 -2.2492757 -3.4825074 -5.7859859 -1.4772208 1.8647427 -5.5206398 1.8961396 0.3861443 -2.6258278
120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136
-1.2608866 7.3346754 -0.3648861 -0.9025271 -8.3644404 -0.2970551 -1.8090516 -4.4254561 0.5833549 -4.5589025 1.1898920 -0.1771279 0.4107215 -0.2915643 1.0268999 1.7803945 -0.9484046
137
0.1661597 merlin_by_all.lm <- lm(response ~ birdlife_pool_size + realm + population_growth + region_100km_cultivated + percentage_urban_area_as_open_public_spaces + biome_name + rainfall_monthly_min + region_20km_average_pop_density+ permanent_water + population_growth + temperature_annual_average + temperature_monthly_min, data = merlin_city_data_fixed)
Error in eval(predvars, data, env) :
object 'birdlife_pool_size' not foundmerlin_by_preferred.lm <- lm(response ~ merlin_pool_size + biome_name + rainfall_monthly_min, data = merlin_city_data_fixed)
merlin_city_data$residuals_preferred <- resid(merlin_by_preferred.lm)
birdlife_by_preferred.lm <- lm(response ~ birdlife_pool_size + biome_name + rainfall_monthly_min, data = birdlife_city_data_fixed)
birdlife_city_data$residuals_preferred <- resid(birdlife_by_preferred.lm)write_csv(ordered_cities, "city_effect_residuals.csv")merlin_city_data$name <- merlin_city_data_2$name
plot_merlin_poolsize <- ggplot(merlin_city_data, aes(y = response, x = merlin_pool_size)) +
geom_smooth(method = "lm", se = F) +
geom_point(aes(color = residuals_pool_size), size = 4) +
geom_label_repel(aes(label = name), size = 4) +
xlab("Pool Size") + ylab("City Random Effect Response") +
guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
labs(title = "Merlin response given pool size")
plot_merlin_poolsize`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 123 unlabeled data points (too many overlaps). Consider increasing max.overlaps
birdlife_city_data$name <- birdlife_city_data_2$name
plot_birdlife_poolsize <- ggplot(birdlife_city_data, aes(y = response, x = birdlife_pool_size)) +
geom_smooth(method = "lm", se = F) +
geom_point(aes(color = residuals_pool_size), size = 4) +
geom_label_repel(aes(label = name), size = 4) +
xlab("Pool Size") + ylab("City Random Effect Response") +
guides(color=guide_legend(title="Model residuals 'response ~ pool_size'")) +
theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
labs(title = "Birdlife response given pool size")
plot_birdlife_poolsize`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 114 unlabeled data points (too many overlaps). Consider increasing max.overlaps
plot_merlin_preferred <- ggplot(merlin_city_data, aes(y = response, x = merlin_pool_size)) +
geom_smooth(method = "lm", se = F) +
geom_point(aes(color = residuals_preferred), size = 4) +
geom_label_repel(aes(label = name), size = 4) +
xlab("Pool Size") + ylab("City Random Effect Response") +
guides(color=guide_legend(title="Model residuals 'response ~ pool_size + biome + rainfall_min'")) +
theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
labs(title = "Merlin response given pool size, biome, and rainfall")
plot_merlin_preferred`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 123 unlabeled data points (too many overlaps). Consider increasing max.overlaps
plot_birdlife_preferred <- ggplot(birdlife_city_data, aes(y = response, x = birdlife_pool_size)) +
geom_smooth(method = "lm", se = F) +
geom_point(aes(color = residuals_preferred), size = 4) +
geom_label_repel(aes(label = name), size = 4) +
xlab("Pool Size") + ylab("City Random Effect Response") +
guides(color=guide_legend(title="Model residuals 'response ~ pool_size + biome + rainfall_min'")) +
theme_bw() + theme(legend.position="bottom", legend.title=element_text(size=9), legend.text=element_text(size=8), legend.key.size = unit(1,"line")) +
labs(title = "Birdlife response given pool size, biome, and rainfall")
plot_birdlife_preferred`geom_smooth()` using formula 'y ~ x'
Warning: ggrepel: 114 unlabeled data points (too many overlaps). Consider increasing max.overlaps
library(ggpubr)plot_residuals <- ggarrange(plot_merlin_poolsize, plot_birdlife_poolsize, plot_merlin_preferred, plot_birdlife_preferred)`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'plot_residualsWarning: ggrepel: 134 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 136 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 134 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 136 unlabeled data points (too many overlaps). Consider increasing max.overlaps
jpeg("city_effect_residuals.jpg", width = 1600, height = 1200)
plot_residualsWarning: ggrepel: 83 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 69 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 83 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Warning: ggrepel: 69 unlabeled data points (too many overlaps). Consider increasing max.overlapsdev.off()null device
1 merlin_city_data_200 <- merlin_city_data[merlin_city_data$merlin_pool_size > 190 & merlin_city_data$merlin_pool_size < 210,]
merlin_city_data_200[order(merlin_city_data_200$response), c("name", "response")]birdlife_city_data_200 <- birdlife_city_data[birdlife_city_data$birdlife_pool_size > 190 & birdlife_city_data$birdlife_pool_size < 210,]
birdlife_city_data_200[order(birdlife_city_data_200$response), c("name", "response")]birdlife_city_data_300 <- birdlife_city_data[birdlife_city_data$birdlife_pool_size > 290 & birdlife_city_data$birdlife_pool_size < 310,]
birdlife_city_data_300[order(birdlife_city_data_300$response), c("name", "response")]merlin_city_data_300 <- merlin_city_data[merlin_city_data$merlin_pool_size > 290 & merlin_city_data$merlin_pool_size < 310,]
merlin_city_data_300[order(merlin_city_data_300$response), c("name", "response")]
ggplot(city_data_subset, aes(x = label, y = response, color = label)) +
geom_label_repel(aes(label = biome_name), size = 3) + geom_point() +
theme_bw() + theme(legend.position = "none") + xlab("Pool size (Pool)") + ylab("Random Effect Response")
Warning: ggrepel: 22 unlabeled data points (too many overlaps). Consider increasing max.overlaps
table(city_data$biome_name)
Boreal Forests/Taiga Deserts & Xeric Shrublands Flooded Grasslands & Savannas
1 11 2
Mangroves Mediterranean Forests, Woodlands & Scrub Montane Grasslands & Shrublands
2 15 2
Temperate Broadleaf & Mixed Forests Temperate Conifer Forests Temperate Grasslands, Savannas & Shrublands
32 2 5
Tropical & Subtropical Coniferous Forests Tropical & Subtropical Dry Broadleaf Forests Tropical & Subtropical Grasslands, Savannas & Shrublands
2 14 5
Tropical & Subtropical Moist Broadleaf Forests
44 summary(glm(response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") + merlin_pool_size, merlin_city_data, family = "gaussian"))
Call:
glm(formula = response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") +
merlin_pool_size, family = "gaussian", data = merlin_city_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-8.0835 -1.9045 -0.2273 1.7119 16.1025
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.885752 1.000848 5.881 3.60e-08 ***
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Boreal Forests/Taiga -5.189947 3.612126 -1.437 0.1533
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Deserts & Xeric Shrublands 1.434085 1.257137 1.141 0.2562
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Flooded Grasslands & Savannas -2.670900 2.586224 -1.033 0.3038
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Mangroves 5.849655 2.586319 2.262 0.0255 *
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Mediterranean Forests, Woodlands & Scrub -0.079682 1.110329 -0.072 0.9429
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Montane Grasslands & Shrublands 0.324356 2.601094 0.125 0.9010
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Temperate Conifer Forests 1.206379 2.590705 0.466 0.6423
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Temperate Grasslands, Savannas & Shrublands 2.811650 1.721678 1.633 0.1050
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Coniferous Forests 5.225315 2.589895 2.018 0.0458 *
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Dry Broadleaf Forests 1.775978 1.150258 1.544 0.1252
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Grasslands, Savannas & Shrublands 2.889845 1.706685 1.693 0.0929 .
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Moist Broadleaf Forests 0.567393 0.845207 0.671 0.5033
merlin_pool_size -0.024120 0.003215 -7.503 1.09e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 12.59017)
Null deviance: 2469.6 on 136 degrees of freedom
Residual deviance: 1548.6 on 123 degrees of freedom
AIC: 751.03
Number of Fisher Scoring iterations: 2summary(glm(response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") + birdlife_pool_size, birdlife_city_data, family = "gaussian"))
Call:
glm(formula = response ~ relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests") +
birdlife_pool_size, family = "gaussian", data = birdlife_city_data)
Deviance Residuals:
Min 1Q Median 3Q Max
-5.1393 -1.3900 -0.1492 0.9716 9.4609
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.610738 0.717523 3.639 0.000402 ***
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Boreal Forests/Taiga -3.270154 2.364009 -1.383 0.169076
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Deserts & Xeric Shrublands 0.847287 0.821355 1.032 0.304297
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Flooded Grasslands & Savannas -1.251925 1.697245 -0.738 0.462149
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Mangroves 2.313444 1.719089 1.346 0.180862
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Mediterranean Forests, Woodlands & Scrub -0.052808 0.730027 -0.072 0.942452
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Montane Grasslands & Shrublands 1.774103 1.761046 1.007 0.315713
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Temperate Conifer Forests 1.175044 1.696677 0.693 0.489894
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Temperate Grasslands, Savannas & Shrublands 1.578314 1.120724 1.408 0.161565
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Coniferous Forests 2.969675 1.708484 1.738 0.084679 .
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Dry Broadleaf Forests 2.104071 0.770869 2.729 0.007274 **
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Grasslands, Savannas & Shrublands 0.669519 1.183055 0.566 0.572477
relevel(biome_name, ref = "Temperate Broadleaf & Mixed Forests")Tropical & Subtropical Moist Broadleaf Forests 0.561698 0.634580 0.885 0.377803
birdlife_pool_size -0.010901 0.002526 -4.315 3.25e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 5.414085)
Null deviance: 865.45 on 136 degrees of freedom
Residual deviance: 665.93 on 123 degrees of freedom
AIC: 635.41
Number of Fisher Scoring iterations: 2unique(city_data$biome_name)
[1] Tropical & Subtropical Grasslands, Savannas & Shrublands Montane Grasslands & Shrublands Tropical & Subtropical Moist Broadleaf Forests
[4] Tropical & Subtropical Dry Broadleaf Forests Mediterranean Forests, Woodlands & Scrub Temperate Broadleaf & Mixed Forests
[7] Temperate Grasslands, Savannas & Shrublands Flooded Grasslands & Savannas Deserts & Xeric Shrublands
[10] Tropical & Subtropical Coniferous Forests Mangroves Boreal Forests/Taiga
[13] Temperate Conifer Forests
13 Levels: Boreal Forests/Taiga Deserts & Xeric Shrublands Flooded Grasslands & Savannas Mangroves Mediterranean Forests, Woodlands & Scrub ... Tropical & Subtropical Moist Broadleaf Forestssummary(glm(response ~ biome_vegetation + biome_location + biome_climate + merlin_pool_size, tmp, family = "gaussian"))
Call:
glm(formula = response ~ biome_vegetation + biome_location +
biome_climate + merlin_pool_size, family = "gaussian", data = tmp)
Deviance Residuals:
Min 1Q Median 3Q Max
-8.0908 -1.9288 -0.1578 1.5228 16.1552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.207549 4.010921 -0.301 0.7639
biome_vegetationGrassland & Shrublands 0.897658 1.393377 0.644 0.5206
biome_locationDesert 7.462201 4.558021 1.637 0.1041
biome_locationGlobal 4.603752 5.033288 0.915 0.3621
biome_locationMediterranean 5.068451 3.681711 1.377 0.1711
biome_locationMontane 4.526499 4.588225 0.987 0.3258
biome_locationTemperate 5.447927 3.616762 1.506 0.1345
biome_locationTropical & Subtropical 8.707133 3.833935 2.271 0.0248 *
biome_climateNormal 1.813755 1.716024 1.057 0.2926
biome_climateWet -1.210677 1.091495 -1.109 0.2695
merlin_pool_size -0.023574 0.003203 -7.360 2.09e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 12.65148)
Null deviance: 2469.6 on 136 degrees of freedom
Residual deviance: 1594.1 on 126 degrees of freedom
AIC: 749
Number of Fisher Scoring iterations: 2summary(glm(response ~ biome_name + rainfall_monthly_min * merlin_pool_size, tmp, family = "gaussian"))
Call:
glm(formula = response ~ biome_name + rainfall_monthly_min *
merlin_pool_size, family = "gaussian", data = tmp)
Deviance Residuals:
Min 1Q Median 3Q Max
-8.1207 -2.0357 -0.2982 1.7386 16.9663
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.7651540 3.7445952 -0.471 0.63821
biome_nameDeserts & Xeric Shrublands 6.9199788 3.7166807 1.862 0.06505 .
biome_nameFlooded Grasslands & Savannas 3.1663312 4.3217462 0.733 0.46519
biome_nameMangroves 11.4058602 4.3149495 2.643 0.00930 **
biome_nameMediterranean Forests, Woodlands & Scrub 5.6330661 3.6528049 1.542 0.12566
biome_nameMontane Grasslands & Shrublands 5.5893414 4.3513235 1.285 0.20142
biome_nameTemperate Broadleaf & Mixed Forests 4.8242325 3.5815194 1.347 0.18050
biome_nameTemperate Conifer Forests 6.3633304 4.3224710 1.472 0.14358
biome_nameTemperate Grasslands, Savannas & Shrublands 7.7518254 3.8814809 1.997 0.04805 *
biome_nameTropical & Subtropical Coniferous Forests 10.7588673 4.3372299 2.481 0.01449 *
biome_nameTropical & Subtropical Dry Broadleaf Forests 7.4313934 3.6779487 2.021 0.04554 *
biome_nameTropical & Subtropical Grasslands, Savannas & Shrublands 8.6265332 3.8689336 2.230 0.02761 *
biome_nameTropical & Subtropical Moist Broadleaf Forests 5.8902326 3.5806727 1.645 0.10256
rainfall_monthly_min 0.0846540 0.0403496 2.098 0.03798 *
merlin_pool_size -0.0169689 0.0050538 -3.358 0.00105 **
rainfall_monthly_min:merlin_pool_size -0.0002442 0.0001253 -1.949 0.05367 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 12.34648)
Null deviance: 2469.6 on 136 degrees of freedom
Residual deviance: 1493.9 on 121 degrees of freedom
AIC: 750.11
Number of Fisher Scoring iterations: 2library(boot)results <- boot(data=merlin_city_data, statistic=rsq, R=1000, formula=response ~ biome_name + merlin_pool_size)
boot.ci(results, type="bca")
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 1000 bootstrap replicates
CALL :
boot.ci(boot.out = results, type = "bca")
Intervals :
Level BCa
95% ( 0.2256, 0.4651 )
Calculations and Intervals on Original Scale
Some BCa intervals may be unstableresults
ORDINARY NONPARAMETRIC BOOTSTRAP
Call:
boot(data = merlin_city_data, statistic = rsq, R = 1000, formula = response ~
biome_name + merlin_pool_size)
Bootstrap Statistics :
original bias std. error
t1* 0.3729407 0.03398538 0.06274843results <- boot(data=merlin_city_data, statistic=rsq, R=1000, formula=response ~ biome_name + rainfall_monthly_min * merlin_pool_size)
boot.ci(results, type="bca")
Warning in norm.inter(t, adj.alpha) :
extreme order statistics used as endpointsBOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 1000 bootstrap replicates
CALL :
boot.ci(boot.out = results, type = "bca")
Intervals :
Level BCa
95% ( 0.2317, 0.4865 )
Calculations and Intervals on Original Scale
Warning : BCa Intervals used Extreme Quantiles
Some BCa intervals may be unstableresults
ORDINARY NONPARAMETRIC BOOTSTRAP
Call:
boot(data = merlin_city_data, statistic = rsq, R = 1000, formula = response ~
biome_name + rainfall_monthly_min * merlin_pool_size)
Bootstrap Statistics :
original bias std. error
t1* 0.3950763 0.04527929 0.06933429results <- boot(data=merlin_city_data, statistic=rsq, R=1000, formula=response ~ merlin_pool_size)
boot.ci(results, type="bca")
BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
Based on 1000 bootstrap replicates
CALL :
boot.ci(boot.out = results, type = "bca")
Intervals :
Level BCa
95% ( 0.1645, 0.3803 )
Calculations and Intervals on Original Scaleresults
ORDINARY NONPARAMETRIC BOOTSTRAP
Call:
boot(data = merlin_city_data, statistic = rsq, R = 1000, formula = response ~
merlin_pool_size)
Bootstrap Statistics :
original bias std. error
t1* 0.275204 0.003607655 0.05538602LS0tCnRpdGxlOiAiUiBOb3RlYm9vayIKb3V0cHV0OiBodG1sX25vdGVib29rCi0tLQpSdW4gYGRvd25sb2FkX2RhdGEuUm1kYCBhbmQgYHBlcmNlbnRhZ2Vfb2ZfcmVnaW9uYWxfcmljaG5lc3MuUm1kYCBGaXJzdCEKCmBgYHtyfQpjaXR5X2RhdGEKYGBgCgpgYGB7cn0KZmV0Y2hfY2l0eV9kYXRhX2ZvciA8LSBmdW5jdGlvbihwb29sX25hbWUsIGluY2x1ZGVfY2l0eV9uYW1lID0gRikgewogIHJlc3VsdHNfZmlsZW5hbWUgPC0gcGFzdGUocGFzdGUocG9vbF9uYW1lLCAnY2l0eScsICdyaWNobmVzcycsICdpbnRlcmNlcHQnLCBzZXAgPSAiXyIpLCAiY3N2Iiwgc2VwID0gIi4iKQogIHJlc3VsdHMgPC0gcmVhZF9jc3YocmVzdWx0c19maWxlbmFtZSkKICAKICBqb2luZWQgPC0gbGVmdF9qb2luKGNpdHlfZGF0YSwgcmVzdWx0cykKICAKICBwb29sX3NpemVfY29sX25hbWUgPC0gcGFzdGUocG9vbF9uYW1lLCAncG9vbCcsICdzaXplJywgc2VwID0gIl8iKQogIAogIHJlcXVpcmVkX2NvbHVtbnMgPC0gYygicmVzcG9uc2UiLCBwb29sX3NpemVfY29sX25hbWUsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJyYWluZmFsbF9hbm51YWxfYXZlcmFnZSIsICJyYWluZmFsbF9tb250aGx5X21heCIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21heCIsICJoYXBwaW5lc3NfbmVnYXRpdmVfZWZmZWN0IiwgImhhcHBpbmVzc19wb3NpdGl2ZV9lZmZlY3QiLCAiaGFwcGluZXNzX2Z1dHVyZV9saWZlIiwgIm51bWJlcl9vZl9iaW9tZXMiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJyZWdpb25fMjBrbV9pbmNsdWRlc19lc3R1YXJ5IiwgInJlZ2lvbl81MGttX2luY2x1ZGVzX2VzdHVhcnkiLCAicmVnaW9uXzEwMGttX2luY2x1ZGVzX2VzdHVhcnkiLCAiY2l0eV9pbmNsdWRlc19lc3R1YXJ5IiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVnaW9uXzUwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJyZWdpb25fMTAwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJjaXR5X21heF9wb3BfZGVuc2l0eSIsICJjaXR5X2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAibWVhbl9wb3B1bGF0aW9uX2V4cG9zdXJlX3RvX3BtMl81XzIwMTkiLCAicmVnaW9uXzIwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyZWdpb25fNTBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl81MGttX3VyYmFuIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8xMDBrbV91cmJhbiIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicmVnaW9uXzIwa21fbWVhbl9lbGV2YXRpb24iLCAicmVnaW9uXzUwa21fZWxldmF0aW9uX2RlbHRhIiwgInJlZ2lvbl81MGttX21lYW5fZWxldmF0aW9uIiwgInJlZ2lvbl8xMDBrbV9lbGV2YXRpb25fZGVsdGEiLCAicmVnaW9uXzEwMGttX21lYW5fZWxldmF0aW9uIiwgImNpdHlfZWxldmF0aW9uX2RlbHRhIiwgImNpdHlfbWVhbl9lbGV2YXRpb24iLCAidXJiYW4iLCAic2hydWJzIiwgInBlcm1hbmVudF93YXRlciIsICJvcGVuX2ZvcmVzdCIsICJoZXJiYWNlb3VzX3dldGxhbmQiLCAiaGVyYmFjZW91c192ZWdldGF0aW9uIiwgImN1bHRpdmF0ZWQiLCAiY2xvc2VkX2ZvcmVzdCIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX3N0cmVldHMiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlc19hbmRfc3RyZWV0cyIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgImNpdHlfZ2RwX3Blcl9wb3B1bGF0aW9uIikKICAKICBpZiAoaW5jbHVkZV9jaXR5X25hbWUpIHsKICAgIHJlcXVpcmVkX2NvbHVtbnMgPC0gYXBwZW5kKGMoIm5hbWUiKSwgcmVxdWlyZWRfY29sdW1ucykKICB9CiAgCiAgam9pbmVkWyxyZXF1aXJlZF9jb2x1bW5zXQp9CmBgYAoKCmBgYHtyfQptZXJsaW5fY2l0eV9kYXRhIDwtIGZldGNoX2NpdHlfZGF0YV9mb3IoJ21lcmxpbicpCm1lcmxpbl9jaXR5X2RhdGEKYGBgCgpgYGB7cn0KbGlicmFyeShyYW5kb21Gb3Jlc3QpCmxpYnJhcnkocmVzaGFwZTIpCmxpYnJhcnkocnBhcnQpCmxpYnJhcnkoZ2dwbG90MikKbGlicmFyeSh0aWR5dmVyc2UpCmBgYAoKYGBge3J9Cm1lcmxpbl9jaXR5X2RhdGFfZml4ZWQgPC0gcmZJbXB1dGUocmVzcG9uc2UgfiAuLCBtZXJsaW5fY2l0eV9kYXRhKQptZXJsaW5fY2l0eV9kYXRhX2ZpeGVkCmBgYAoKCmBgYHtyfQpzb3VyY2UoJy4vcmFuZG9tX2ZvcmVzdF9zZWxlY3Rpb25fZnVuY3Rpb25zLlInKQpgYGAKCmBgYHtyfQpzZWxlY3RfdmFyaWFibGVzX2Zyb21fcmFuZG9tX2ZvcmVzdChtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkKQpgYGAKCmBgYHtyfQpleGNsdWRlX21lcmxpbiA8LSAhbmFtZXMobWVybGluX2NpdHlfZGF0YV9maXhlZCkgJWluJSBjKAogICJyZWdpb25fNTBrbV91cmJhbiIsICJyZWdpb25fMTAwa21fdXJiYW4iLCAKICAicmVnaW9uXzUwa21fZWxldmF0aW9uX2RlbHRhIiwgInJlZ2lvbl8xMDBrbV9lbGV2YXRpb25fZGVsdGEiLCAKICAicmVnaW9uXzUwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsIAogICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJlZ2lvbl8xMDBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgCiAgInJlZ2lvbl8yMGttX2luY2x1ZGVzX2VzdHVhcnkiLCAicmVnaW9uXzUwa21faW5jbHVkZXNfZXN0dWFyeSIsICJyZWdpb25fMTAwa21faW5jbHVkZXNfZXN0dWFyeSIsIAogICJyZWdpb25fMTAwa21fbWVhbl9lbGV2YXRpb24iLCAicmVnaW9uXzIwa21fbWVhbl9lbGV2YXRpb24iKQoKbWVybGluX2NpdHlfZGF0YV9maXhlZF9zaW5nbGVfc2NhbGUgPC0gbWVybGluX2NpdHlfZGF0YV9maXhlZFssZXhjbHVkZV9tZXJsaW5dCm1lcmxpbl9jaXR5X2RhdGFfZml4ZWRfc2luZ2xlX3NjYWxlCmBgYAoKYGBge3J9CnNlbGVjdF92YXJpYWJsZXNfZnJvbV9yYW5kb21fZm9yZXN0KG1lcmxpbl9jaXR5X2RhdGFfZml4ZWRfc2luZ2xlX3NjYWxlKQpgYGAKCmBgYHtyfQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IobWVybGluX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAibWVybGluX3Bvb2xfc2l6ZSIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKG1lcmxpbl9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgIm1lcmxpbl9wb29sX3NpemUiLCAicmVhbG0iKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAiaGFwcGluZXNzX3Bvc2l0aXZlX2VmZmVjdCIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKG1lcmxpbl9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgIm1lcmxpbl9wb29sX3NpemUiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJoYXBwaW5lc3NfcG9zaXRpdmVfZWZmZWN0IiwgInJlZ2lvbl8yMGttX2VsZXZhdGlvbl9kZWx0YSIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKG1lcmxpbl9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgIm1lcmxpbl9wb29sX3NpemUiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJoYXBwaW5lc3NfcG9zaXRpdmVfZWZmZWN0IiwgInJlZ2lvbl8yMGttX2VsZXZhdGlvbl9kZWx0YSIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IobWVybGluX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAibWVybGluX3Bvb2xfc2l6ZSIsICJyZWFsbSIsICJiaW9tZV9uYW1lIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgImhhcHBpbmVzc19wb3NpdGl2ZV9lZmZlY3QiLCAicmVnaW9uXzIwa21fZWxldmF0aW9uX2RlbHRhIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAicmFpbmZhbGxfbW9udGhseV9taW4iKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAiaGFwcGluZXNzX3Bvc2l0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAiaGFwcGluZXNzX3Bvc2l0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAiaGFwcGluZXNzX3Bvc2l0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fdXJiYW4iKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAiaGFwcGluZXNzX3Bvc2l0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fdXJiYW4iLCAic2hydWJzIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IobWVybGluX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAibWVybGluX3Bvb2xfc2l6ZSIsICJyZWFsbSIsICJiaW9tZV9uYW1lIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgImhhcHBpbmVzc19wb3NpdGl2ZV9lZmZlY3QiLCAicmVnaW9uXzIwa21fZWxldmF0aW9uX2RlbHRhIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAicGVybWFuZW50X3dhdGVyIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX3VyYmFuIiwgInNocnVicyIsICJyZWdpb25fMjBrbV9jdWx0aXZhdGVkIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IobWVybGluX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAibWVybGluX3Bvb2xfc2l6ZSIsICJyZWFsbSIsICJiaW9tZV9uYW1lIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgImhhcHBpbmVzc19wb3NpdGl2ZV9lZmZlY3QiLCAicmVnaW9uXzIwa21fZWxldmF0aW9uX2RlbHRhIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAicGVybWFuZW50X3dhdGVyIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX3VyYmFuIiwgInNocnVicyIsICJyZWdpb25fMjBrbV9jdWx0aXZhdGVkIiwgImhhcHBpbmVzc19uZWdhdGl2ZV9lZmZlY3QiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAiaGFwcGluZXNzX3Bvc2l0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fdXJiYW4iLCAic2hydWJzIiwgInJlZ2lvbl8yMGttX2N1bHRpdmF0ZWQiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAiaGFwcGluZXNzX3Bvc2l0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fdXJiYW4iLCAic2hydWJzIiwgInJlZ2lvbl8yMGttX2N1bHRpdmF0ZWQiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiLCAidGVtcGVyYXR1cmVfbW9udGhseV9tYXgiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAiaGFwcGluZXNzX3Bvc2l0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fdXJiYW4iLCAic2hydWJzIiwgInJlZ2lvbl8yMGttX2N1bHRpdmF0ZWQiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiLCAidGVtcGVyYXR1cmVfbW9udGhseV9tYXgiLCAicmFpbmZhbGxfbW9udGhseV9tYXgiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJtZXJsaW5fcG9vbF9zaXplIiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAiaGFwcGluZXNzX3Bvc2l0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fdXJiYW4iLCAic2hydWJzIiwgInJlZ2lvbl8yMGttX2N1bHRpdmF0ZWQiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiLCAidGVtcGVyYXR1cmVfbW9udGhseV9tYXgiLCAicmFpbmZhbGxfbW9udGhseV9tYXgiLCAicmFpbmZhbGxfYW5udWFsX2F2ZXJhZ2UiKV0pCmBgYAoKIm1lcmxpbl9wb29sX3NpemUiLCAicmVhbG0iCgoKYGBge3J9CmJpcmRsaWZlX2NpdHlfZGF0YSA8LSBmZXRjaF9jaXR5X2RhdGFfZm9yKCdiaXJkbGlmZScpCmJpcmRsaWZlX2NpdHlfZGF0YQpgYGAKCmBgYHtyfQpiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWQgPC0gcmZJbXB1dGUocmVzcG9uc2UgfiAuLCBiaXJkbGlmZV9jaXR5X2RhdGEpCmJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZApgYGAKCmBgYHtyfQpzZWxlY3RfdmFyaWFibGVzX2Zyb21fcmFuZG9tX2ZvcmVzdChiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWQpCmBgYAoKYGBge3J9CmV4Y2x1ZGVfYmlyZGxpZmUgPC0gIW5hbWVzKGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZCkgJWluJSBjKAogICJyZWdpb25fNTBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8yMGttX2N1bHRpdmF0ZWQiLCAKICAicmVnaW9uXzEwMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVnaW9uXzUwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsIAogICJyZWdpb25fNTBrbV91cmJhbiIsICJyZWdpb25fMjBrbV91cmJhbiIsIAogICJyZWdpb25fMTAwa21fZWxldmF0aW9uX2RlbHRhIiwgInJlZ2lvbl81MGttX2VsZXZhdGlvbl9kZWx0YSIsIAogICJyZWdpb25fMjBrbV9pbmNsdWRlc19lc3R1YXJ5IiwgInJlZ2lvbl81MGttX2luY2x1ZGVzX2VzdHVhcnkiLCAicmVnaW9uXzEwMGttX2luY2x1ZGVzX2VzdHVhcnkiLCAKICAicmVnaW9uXzEwMGttX21lYW5fZWxldmF0aW9uIiwgInJlZ2lvbl8yMGttX21lYW5fZWxldmF0aW9uIikKCmJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZF9zaW5nbGVfc2NhbGUgPC0gYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxleGNsdWRlX2JpcmRsaWZlXQpiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWRfc2luZ2xlX3NjYWxlCmBgYAoKYGBge3J9CnNlbGVjdF92YXJpYWJsZXNfZnJvbV9yYW5kb21fZm9yZXN0KGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZF9zaW5nbGVfc2NhbGUpCmBgYAoKYGBge3J9CmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgInBvcHVsYXRpb25fZ3Jvd3RoIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgImJpcmRsaWZlX3Bvb2xfc2l6ZSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAiYmlyZGxpZmVfcG9vbF9zaXplIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAiYmlvbWVfbmFtZSIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAiYmlyZGxpZmVfcG9vbF9zaXplIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAiYmlyZGxpZmVfcG9vbF9zaXplIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIiwgInJhaW5mYWxsX21vbnRobHlfbWF4IildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIiwgInJhaW5mYWxsX21vbnRobHlfbWF4IiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIiwgInJhaW5mYWxsX21vbnRobHlfbWF4IiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIiwgInJhaW5mYWxsX21vbnRobHlfbWF4IiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgIm1lYW5fcG9wdWxhdGlvbl9leHBvc3VyZV90b19wbTJfNV8yMDE5IildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIiwgInJhaW5mYWxsX21vbnRobHlfbWF4IiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgIm1lYW5fcG9wdWxhdGlvbl9leHBvc3VyZV90b19wbTJfNV8yMDE5IiwgInJlZ2lvbl8xMDBrbV91cmJhbiIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAiYmlyZGxpZmVfcG9vbF9zaXplIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInBlcm1hbmVudF93YXRlciIsICJyYWluZmFsbF9tb250aGx5X21heCIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJtZWFuX3BvcHVsYXRpb25fZXhwb3N1cmVfdG9fcG0yXzVfMjAxOSIsICJyZWdpb25fMTAwa21fdXJiYW4iLCAic2hydWJzIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIiwgInJhaW5mYWxsX21vbnRobHlfbWF4IiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgIm1lYW5fcG9wdWxhdGlvbl9leHBvc3VyZV90b19wbTJfNV8yMDE5IiwgInJlZ2lvbl8xMDBrbV91cmJhbiIsICJzaHJ1YnMiLCAicmVnaW9uXzIwa21fZWxldmF0aW9uX2RlbHRhIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIiwgInJhaW5mYWxsX21vbnRobHlfbWF4IiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgIm1lYW5fcG9wdWxhdGlvbl9leHBvc3VyZV90b19wbTJfNV8yMDE5IiwgInJlZ2lvbl8xMDBrbV91cmJhbiIsICJzaHJ1YnMiLCAicmVnaW9uXzIwa21fZWxldmF0aW9uX2RlbHRhIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXNfYW5kX3N0cmVldHMiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgImJpcmRsaWZlX3Bvb2xfc2l6ZSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJwZXJtYW5lbnRfd2F0ZXIiLCAicmFpbmZhbGxfbW9udGhseV9tYXgiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAibWVhbl9wb3B1bGF0aW9uX2V4cG9zdXJlX3RvX3BtMl81XzIwMTkiLCAicmVnaW9uXzEwMGttX3VyYmFuIiwgInNocnVicyIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlc19hbmRfc3RyZWV0cyIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgImJpcmRsaWZlX3Bvb2xfc2l6ZSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJwZXJtYW5lbnRfd2F0ZXIiLCAicmFpbmZhbGxfbW9udGhseV9tYXgiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAibWVhbl9wb3B1bGF0aW9uX2V4cG9zdXJlX3RvX3BtMl81XzIwMTkiLCAicmVnaW9uXzEwMGttX3VyYmFuIiwgInNocnVicyIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlc19hbmRfc3RyZWV0cyIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiLCAicmVhbG0iKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgImJpcmRsaWZlX3Bvb2xfc2l6ZSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJwZXJtYW5lbnRfd2F0ZXIiLCAicmFpbmZhbGxfbW9udGhseV9tYXgiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAibWVhbl9wb3B1bGF0aW9uX2V4cG9zdXJlX3RvX3BtMl81XzIwMTkiLCAicmVnaW9uXzEwMGttX3VyYmFuIiwgInNocnVicyIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlc19hbmRfc3RyZWV0cyIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiLCAicmVhbG0iLCAiY2l0eV9hdmVyYWdlX3BvcF9kZW5zaXR5IildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIiwgInJhaW5mYWxsX21vbnRobHlfbWF4IiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgIm1lYW5fcG9wdWxhdGlvbl9leHBvc3VyZV90b19wbTJfNV8yMDE5IiwgInJlZ2lvbl8xMDBrbV91cmJhbiIsICJzaHJ1YnMiLCAicmVnaW9uXzIwa21fZWxldmF0aW9uX2RlbHRhIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXNfYW5kX3N0cmVldHMiLCAic2hhcmVfb2ZfcG9wdWxhdGlvbl93aXRoaW5fNDAwbV9vZl9vcGVuX3NwYWNlIiwgInJlYWxtIiwgImNpdHlfYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJvcGVuX2ZvcmVzdCIsICJoYXBwaW5lc3NfZnV0dXJlX2xpZmUiKV0pCmBgYAoKInBvcHVsYXRpb25fZ3Jvd3RoIiwgImJpcmRsaWZlX3Bvb2xfc2l6ZSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJwZXJtYW5lbnRfd2F0ZXIiCgpgYGB7cn0KZWl0aGVyX2NpdHlfZGF0YSA8LSBmZXRjaF9jaXR5X2RhdGFfZm9yKCdlaXRoZXInKQplaXRoZXJfY2l0eV9kYXRhCmBgYAoKYGBge3J9CmVpdGhlcl9jaXR5X2RhdGFfZml4ZWQgPC0gcmZJbXB1dGUocmVzcG9uc2UgfiAuLCBlaXRoZXJfY2l0eV9kYXRhKQplaXRoZXJfY2l0eV9kYXRhX2ZpeGVkCmBgYAoKYGBge3J9CnNlbGVjdF92YXJpYWJsZXNfZnJvbV9yYW5kb21fZm9yZXN0KGVpdGhlcl9jaXR5X2RhdGFfZml4ZWQpCmBgYAoKYGBge3J9CmV4Y2x1ZGVfZWl0aGVyIDwtICFuYW1lcyhlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkKSAlaW4lIGMoCiAgInJlZ2lvbl81MGttX2N1bHRpdmF0ZWQiLCAicmVnaW9uXzIwa21fY3VsdGl2YXRlZCIsIAogICJyZWdpb25fNTBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJlZ2lvbl8xMDBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgCiAgInJlZ2lvbl8yMGttX2VsZXZhdGlvbl9kZWx0YSIsICJyZWdpb25fMTAwa21fZWxldmF0aW9uX2RlbHRhIiwgCiAgInJlZ2lvbl81MGttX3VyYmFuIiwgInJlZ2lvbl8xMDBrbV91cmJhbiIsIAogICJyZWdpb25fMjBrbV9pbmNsdWRlc19lc3R1YXJ5IiwgInJlZ2lvbl81MGttX2luY2x1ZGVzX2VzdHVhcnkiLCAicmVnaW9uXzEwMGttX2luY2x1ZGVzX2VzdHVhcnkiLCAKICAicmVnaW9uXzEwMGttX21lYW5fZWxldmF0aW9uIiwgInJlZ2lvbl81MGttX21lYW5fZWxldmF0aW9uIikKCmVpdGhlcl9jaXR5X2RhdGFfZml4ZWRfc2luZ2xlX3NjYWxlIDwtIGVpdGhlcl9jaXR5X2RhdGFfZml4ZWRbLGV4Y2x1ZGVfZWl0aGVyXQplaXRoZXJfY2l0eV9kYXRhX2ZpeGVkX3NpbmdsZV9zY2FsZQpgYGAKYGBge3J9CnNlbGVjdF92YXJpYWJsZXNfZnJvbV9yYW5kb21fZm9yZXN0KGVpdGhlcl9jaXR5X2RhdGFfZml4ZWRfc2luZ2xlX3NjYWxlKQpgYGAKCgpgYGB7cn0KY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGVpdGhlcl9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImVpdGhlcl9wb29sX3NpemUiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJlaXRoZXJfcG9vbF9zaXplIiwgInBvcHVsYXRpb25fZ3Jvd3RoIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoZWl0aGVyX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAiZWl0aGVyX3Bvb2xfc2l6ZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGVpdGhlcl9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImVpdGhlcl9wb29sX3NpemUiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGVpdGhlcl9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImVpdGhlcl9wb29sX3NpemUiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJyZWFsbSIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGVpdGhlcl9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImVpdGhlcl9wb29sX3NpemUiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJyZWFsbSIsICJiaW9tZV9uYW1lIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoZWl0aGVyX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAiZWl0aGVyX3Bvb2xfc2l6ZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJlaXRoZXJfcG9vbF9zaXplIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJzaHJ1YnMiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJlaXRoZXJfcG9vbF9zaXplIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJzaHJ1YnMiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJlaXRoZXJfcG9vbF9zaXplIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJzaHJ1YnMiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicGVybWFuZW50X3dhdGVyIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoZWl0aGVyX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAiZWl0aGVyX3Bvb2xfc2l6ZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAic2hydWJzIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInBlcm1hbmVudF93YXRlciIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoZWl0aGVyX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAiZWl0aGVyX3Bvb2xfc2l6ZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAic2hydWJzIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInBlcm1hbmVudF93YXRlciIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgInJlZ2lvbl8yMGttX3VyYmFuIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoZWl0aGVyX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAiZWl0aGVyX3Bvb2xfc2l6ZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAic2hydWJzIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInBlcm1hbmVudF93YXRlciIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgInJlZ2lvbl8yMGttX3VyYmFuIiwgInJlZ2lvbl81MGttX2VsZXZhdGlvbl9kZWx0YSIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGVpdGhlcl9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImVpdGhlcl9wb29sX3NpemUiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJyZWFsbSIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInNocnVicyIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyZWdpb25fNTBrbV9lbGV2YXRpb25fZGVsdGEiLCAibWVhbl9wb3B1bGF0aW9uX2V4cG9zdXJlX3RvX3BtMl81XzIwMTkiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJlaXRoZXJfcG9vbF9zaXplIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJzaHJ1YnMiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicGVybWFuZW50X3dhdGVyIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAicmVnaW9uXzIwa21fdXJiYW4iLCAicmVnaW9uXzUwa21fZWxldmF0aW9uX2RlbHRhIiwgIm1lYW5fcG9wdWxhdGlvbl9leHBvc3VyZV90b19wbTJfNV8yMDE5IiwgImNpdHlfYXZlcmFnZV9wb3BfZGVuc2l0eSIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGVpdGhlcl9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImVpdGhlcl9wb29sX3NpemUiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJyZWFsbSIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInNocnVicyIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyZWdpb25fNTBrbV9lbGV2YXRpb25fZGVsdGEiLCAibWVhbl9wb3B1bGF0aW9uX2V4cG9zdXJlX3RvX3BtMl81XzIwMTkiLCAiY2l0eV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJhaW5mYWxsX21vbnRobHlfbWF4IildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoZWl0aGVyX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAiZWl0aGVyX3Bvb2xfc2l6ZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAic2hydWJzIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInBlcm1hbmVudF93YXRlciIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgInJlZ2lvbl8yMGttX3VyYmFuIiwgInJlZ2lvbl81MGttX2VsZXZhdGlvbl9kZWx0YSIsICJtZWFuX3BvcHVsYXRpb25fZXhwb3N1cmVfdG9fcG0yXzVfMjAxOSIsICJjaXR5X2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmFpbmZhbGxfbW9udGhseV9tYXgiLCAiaGFwcGluZXNzX2Z1dHVyZV9saWZlIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoZWl0aGVyX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAiZWl0aGVyX3Bvb2xfc2l6ZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJlYWxtIiwgImJpb21lX25hbWUiLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAic2hydWJzIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInBlcm1hbmVudF93YXRlciIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgInJlZ2lvbl8yMGttX3VyYmFuIiwgInJlZ2lvbl81MGttX2VsZXZhdGlvbl9kZWx0YSIsICJtZWFuX3BvcHVsYXRpb25fZXhwb3N1cmVfdG9fcG0yXzVfMjAxOSIsICJjaXR5X2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmFpbmZhbGxfbW9udGhseV9tYXgiLCAiaGFwcGluZXNzX2Z1dHVyZV9saWZlIiwgImN1bHRpdmF0ZWQiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJlaXRoZXJfcG9vbF9zaXplIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJzaHJ1YnMiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicGVybWFuZW50X3dhdGVyIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAicmVnaW9uXzIwa21fdXJiYW4iLCAicmVnaW9uXzUwa21fZWxldmF0aW9uX2RlbHRhIiwgIm1lYW5fcG9wdWxhdGlvbl9leHBvc3VyZV90b19wbTJfNV8yMDE5IiwgImNpdHlfYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJyYWluZmFsbF9tb250aGx5X21heCIsICJoYXBwaW5lc3NfZnV0dXJlX2xpZmUiLCAiY3VsdGl2YXRlZCIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJlaXRoZXJfcG9vbF9zaXplIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJzaHJ1YnMiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicGVybWFuZW50X3dhdGVyIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAicmVnaW9uXzIwa21fdXJiYW4iLCAicmVnaW9uXzUwa21fZWxldmF0aW9uX2RlbHRhIiwgIm1lYW5fcG9wdWxhdGlvbl9leHBvc3VyZV90b19wbTJfNV8yMDE5IiwgImNpdHlfYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJyYWluZmFsbF9tb250aGx5X21heCIsICJoYXBwaW5lc3NfZnV0dXJlX2xpZmUiLCAiY3VsdGl2YXRlZCIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiLCAiY2l0eV9lbGV2YXRpb25fZGVsdGEiKV0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJlaXRoZXJfcG9vbF9zaXplIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVhbG0iLCAiYmlvbWVfbmFtZSIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJzaHJ1YnMiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicGVybWFuZW50X3dhdGVyIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAicmVnaW9uXzIwa21fdXJiYW4iLCAicmVnaW9uXzUwa21fZWxldmF0aW9uX2RlbHRhIiwgIm1lYW5fcG9wdWxhdGlvbl9leHBvc3VyZV90b19wbTJfNV8yMDE5IiwgImNpdHlfYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJyYWluZmFsbF9tb250aGx5X21heCIsICJoYXBwaW5lc3NfZnV0dXJlX2xpZmUiLCAiY3VsdGl2YXRlZCIsICJzaGFyZV9vZl9wb3B1bGF0aW9uX3dpdGhpbl80MDBtX29mX29wZW5fc3BhY2UiLCAiY2l0eV9lbGV2YXRpb25fZGVsdGEiLCAiIildKQpgYGAKCiJlaXRoZXJfcG9vbF9zaXplIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVhbG0iCgpgYGB7cn0KYm90aF9jaXR5X2RhdGEgPC0gZmV0Y2hfY2l0eV9kYXRhX2ZvcignYm90aCcpCmJvdGhfY2l0eV9kYXRhCmBgYAoKYGBge3J9CmJvdGhfY2l0eV9kYXRhX2ZpeGVkIDwtIHJmSW1wdXRlKHJlc3BvbnNlIH4gLiwgYm90aF9jaXR5X2RhdGEpCmJvdGhfY2l0eV9kYXRhX2ZpeGVkCmBgYAoKYGBge3J9CnNlbGVjdF92YXJpYWJsZXNfZnJvbV9yYW5kb21fZm9yZXN0KGJvdGhfY2l0eV9kYXRhX2ZpeGVkKQpgYGAKCmBgYHtyfQpleGNsdWRlX2JvdGggPC0gIW5hbWVzKGJvdGhfY2l0eV9kYXRhX2ZpeGVkKSAlaW4lIGMoCiAgInJlZ2lvbl81MGttX2N1bHRpdmF0ZWQiLCAicmVnaW9uXzIwa21fY3VsdGl2YXRlZCIsIAogICJyZWdpb25fNTBrbV91cmJhbiIsICJyZWdpb25fMTAwa21fdXJiYW4iLCAKICAicmVnaW9uXzEwMGttX2VsZXZhdGlvbl9kZWx0YSIsICJyZWdpb25fMjBrbV9lbGV2YXRpb25fZGVsdGEiLCAKICAicmVnaW9uXzEwMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicmVnaW9uXzUwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsIAogICJyZWdpb25fMjBrbV9pbmNsdWRlc19lc3R1YXJ5IiwgInJlZ2lvbl81MGttX2luY2x1ZGVzX2VzdHVhcnkiLCAicmVnaW9uXzEwMGttX2luY2x1ZGVzX2VzdHVhcnkiLCAKICAicmVnaW9uXzEwMGttX21lYW5fZWxldmF0aW9uIiwgInJlZ2lvbl81MGttX21lYW5fZWxldmF0aW9uIikKCmJvdGhfY2l0eV9kYXRhX2ZpeGVkX3NpbmdsZV9zY2FsZSA8LSBib3RoX2NpdHlfZGF0YV9maXhlZFssZXhjbHVkZV9ib3RoXQpib3RoX2NpdHlfZGF0YV9maXhlZF9zaW5nbGVfc2NhbGUKYGBgCgpgYGB7cn0Kc2VsZWN0X3ZhcmlhYmxlc19mcm9tX3JhbmRvbV9mb3Jlc3QoYm90aF9jaXR5X2RhdGFfZml4ZWRfc2luZ2xlX3NjYWxlKQpgYGAKCmBgYHtyfQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYm90aF9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImJvdGhfcG9vbF9zaXplIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYm90aF9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImJvdGhfcG9vbF9zaXplIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYm90aF9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImJvdGhfcG9vbF9zaXplIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIildKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYm90aF9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImJvdGhfcG9vbF9zaXplIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInBlcm1hbmVudF93YXRlciIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJvdGhfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJib3RoX3Bvb2xfc2l6ZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJvdGhfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJib3RoX3Bvb2xfc2l6ZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV91cmJhbiIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJvdGhfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJib3RoX3Bvb2xfc2l6ZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyYWluZmFsbF9tb250aGx5X21pbiIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJvdGhfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJib3RoX3Bvb2xfc2l6ZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJyZWFsbSIpXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJvdGhfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJib3RoX3Bvb2xfc2l6ZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJyZWFsbSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIpLF0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihib3RoX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAiYm90aF9wb29sX3NpemUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicGVybWFuZW50X3dhdGVyIiwgImhhcHBpbmVzc19uZWdhdGl2ZV9lZmZlY3QiLCAicmVnaW9uXzIwa21fdXJiYW4iLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAicmVhbG0iLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicmVnaW9uXzUwa21fZWxldmF0aW9uX2RlbHRhIiksXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJvdGhfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJib3RoX3Bvb2xfc2l6ZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJyZWFsbSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fNTBrbV9lbGV2YXRpb25fZGVsdGEiLCAicG9wdWxhdGlvbl9ncm93dGgiKSxdKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYm90aF9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImJvdGhfcG9vbF9zaXplIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInBlcm1hbmVudF93YXRlciIsICJoYXBwaW5lc3NfbmVnYXRpdmVfZWZmZWN0IiwgInJlZ2lvbl8yMGttX3VyYmFuIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlYWxtIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl81MGttX2VsZXZhdGlvbl9kZWx0YSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiksXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJvdGhfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJib3RoX3Bvb2xfc2l6ZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJyZWFsbSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fNTBrbV9lbGV2YXRpb25fZGVsdGEiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJzaHJ1YnMiKSxdKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYm90aF9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImJvdGhfcG9vbF9zaXplIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInBlcm1hbmVudF93YXRlciIsICJoYXBwaW5lc3NfbmVnYXRpdmVfZWZmZWN0IiwgInJlZ2lvbl8yMGttX3VyYmFuIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlYWxtIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl81MGttX2VsZXZhdGlvbl9kZWx0YSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgInNocnVicyIsICJiaW9tZV9uYW1lIiksXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJvdGhfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJib3RoX3Bvb2xfc2l6ZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJyZWFsbSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fNTBrbV9lbGV2YXRpb25fZGVsdGEiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJzaHJ1YnMiLCAiYmlvbWVfbmFtZSIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiksXSkKY3JlYXRlX2ZpZnR5X3Jvd3Nfb2Zfb29iKGJvdGhfY2l0eV9kYXRhX2ZpeGVkWyxjKCJyZXNwb25zZSIsICJib3RoX3Bvb2xfc2l6ZSIsICJ0ZW1wZXJhdHVyZV9hbm51YWxfYXZlcmFnZSIsICJ0ZW1wZXJhdHVyZV9tb250aGx5X21pbiIsICJwZXJtYW5lbnRfd2F0ZXIiLCAiaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCIsICJyZWdpb25fMjBrbV91cmJhbiIsICJyYWluZmFsbF9tb250aGx5X21pbiIsICJyZWFsbSIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fNTBrbV9lbGV2YXRpb25fZGVsdGEiLCAicG9wdWxhdGlvbl9ncm93dGgiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJzaHJ1YnMiLCAiYmlvbWVfbmFtZSIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgImNpdHlfbWVhbl9lbGV2YXRpb24iKSxdKQpjcmVhdGVfZmlmdHlfcm93c19vZl9vb2IoYm90aF9jaXR5X2RhdGFfZml4ZWRbLGMoInJlc3BvbnNlIiwgImJvdGhfcG9vbF9zaXplIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgInRlbXBlcmF0dXJlX21vbnRobHlfbWluIiwgInBlcm1hbmVudF93YXRlciIsICJoYXBwaW5lc3NfbmVnYXRpdmVfZWZmZWN0IiwgInJlZ2lvbl8yMGttX3VyYmFuIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlYWxtIiwgInJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkIiwgInJlZ2lvbl81MGttX2VsZXZhdGlvbl9kZWx0YSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzIiwgInNocnVicyIsICJiaW9tZV9uYW1lIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAiY2l0eV9tZWFuX2VsZXZhdGlvbiIsICJjaXR5X2dkcF9wZXJfcG9wdWxhdGlvbiIpLF0pCmNyZWF0ZV9maWZ0eV9yb3dzX29mX29vYihib3RoX2NpdHlfZGF0YV9maXhlZFssYygicmVzcG9uc2UiLCAiYm90aF9wb29sX3NpemUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iLCAicGVybWFuZW50X3dhdGVyIiwgImhhcHBpbmVzc19uZWdhdGl2ZV9lZmZlY3QiLCAicmVnaW9uXzIwa21fdXJiYW4iLCAicmFpbmZhbGxfbW9udGhseV9taW4iLCAicmVhbG0iLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicmVnaW9uXzUwa21fZWxldmF0aW9uX2RlbHRhIiwgInBvcHVsYXRpb25fZ3Jvd3RoIiwgInBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMiLCAic2hydWJzIiwgImJpb21lX25hbWUiLCAicmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSIsICJjaXR5X21lYW5fZWxldmF0aW9uIiwgImNpdHlfZ2RwX3Blcl9wb3B1bGF0aW9uIiwgInNoYXJlX29mX3BvcHVsYXRpb25fd2l0aGluXzQwMG1fb2Zfb3Blbl9zcGFjZSIpLF0pCmBgYAoKImJvdGhfcG9vbF9zaXplIiwgInRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlIiwgImhhcHBpbmVzc19uZWdhdGl2ZV9lZmZlY3QiCgoKLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tClNvLi4uLgotLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0KIm1lcmxpbl9wb29sX3NpemUiLCAicmVhbG0iCiJwb3B1bGF0aW9uX2dyb3d0aCIsICJiaXJkbGlmZV9wb29sX3NpemUiLCAicmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQiLCAicGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcyIsICJiaW9tZV9uYW1lIiwgInJhaW5mYWxsX21vbnRobHlfbWluIiwgInJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkiLCAicGVybWFuZW50X3dhdGVyIgoiZWl0aGVyX3Bvb2xfc2l6ZSIsICJwb3B1bGF0aW9uX2dyb3d0aCIsICJyZWdpb25fMTAwa21fY3VsdGl2YXRlZCIsICJyZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5IiwgInJlYWxtIgoiYm90aF9wb29sX3NpemUiLCAidGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UiLCAidGVtcGVyYXR1cmVfbW9udGhseV9taW4iCgpgYGB7cn0Kc3VtbWFyeShsbShyZXNwb25zZSB+IG1lcmxpbl9wb29sX3NpemUsIG1lcmxpbl9jaXR5X2RhdGFfZml4ZWQpKQpzdW1tYXJ5KGxtKHJlc3BvbnNlIH4gYmlyZGxpZmVfcG9vbF9zaXplLCBiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWQpKQpzdW1tYXJ5KGxtKHJlc3BvbnNlIH4gZWl0aGVyX3Bvb2xfc2l6ZSwgZWl0aGVyX2NpdHlfZGF0YV9maXhlZCkpCnN1bW1hcnkobG0ocmVzcG9uc2UgfiBib3RoX3Bvb2xfc2l6ZSwgYm90aF9jaXR5X2RhdGFfZml4ZWQpKQpgYGAKCmBgYHtyfQpnZ3Bsb3QoKSArIAogIGdlb21fcG9pbnQoYWVzKHggPSBtZXJsaW5fcG9vbF9zaXplLCB5ID0gcmVzcG9uc2UpLCBtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJyZWQiKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IGJpcmRsaWZlX3Bvb2xfc2l6ZSwgeSA9IHJlc3BvbnNlKSwgYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJibHVlIikgKwogIGdlb21fcG9pbnQoYWVzKHggPSBlaXRoZXJfcG9vbF9zaXplLCB5ID0gcmVzcG9uc2UpLCBlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJncmVlbiIpICsKICBnZW9tX3BvaW50KGFlcyh4ID0gYm90aF9wb29sX3NpemUsIHkgPSByZXNwb25zZSksIGJvdGhfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJwdXJwbGUiKQpgYGAKCmBgYHtyfQpzdW1tYXJ5KGxtKHJlc3BvbnNlIH4gcmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQsIG1lcmxpbl9jaXR5X2RhdGFfZml4ZWQpKQpzdW1tYXJ5KGxtKHJlc3BvbnNlIH4gcmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQsIGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZCkpCnN1bW1hcnkobG0ocmVzcG9uc2UgfiByZWdpb25fMTAwa21fY3VsdGl2YXRlZCwgZWl0aGVyX2NpdHlfZGF0YV9maXhlZCkpCnN1bW1hcnkobG0ocmVzcG9uc2UgfiByZWdpb25fMTAwa21fY3VsdGl2YXRlZCwgYm90aF9jaXR5X2RhdGFfZml4ZWQpKQpgYGAKCgpgYGB7cn0KZ2dwbG90KCkgKyAKICBnZW9tX3BvaW50KGFlcyh4ID0gbWVybGluX3Bvb2xfc2l6ZSwgeSA9IHJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkKSwgbWVybGluX2NpdHlfZGF0YV9maXhlZCwgY29sb3IgPSAicmVkIikgKwogIGdlb21fcG9pbnQoYWVzKHggPSBiaXJkbGlmZV9wb29sX3NpemUsIHkgPSByZWdpb25fMTAwa21fY3VsdGl2YXRlZCksIGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZCwgY29sb3IgPSAiYmx1ZSIpICsKICBnZW9tX3BvaW50KGFlcyh4ID0gZWl0aGVyX3Bvb2xfc2l6ZSwgeSA9IHJlZ2lvbl8xMDBrbV9jdWx0aXZhdGVkKSwgZWl0aGVyX2NpdHlfZGF0YV9maXhlZCwgY29sb3IgPSAiZ3JlZW4iKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IGJvdGhfcG9vbF9zaXplLCB5ID0gcmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQpLCBib3RoX2NpdHlfZGF0YV9maXhlZCwgY29sb3IgPSAicHVycGxlIikKYGBgCgpgYGB7cn0KZ2dwbG90KCkgKyAKICBnZW9tX3BvaW50KGFlcyh4ID0gcG9wdWxhdGlvbl9ncm93dGgsIHkgPSByZXNwb25zZSksIG1lcmxpbl9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gInJlZCIpICsKICBnZW9tX3BvaW50KGFlcyh4ID0gcG9wdWxhdGlvbl9ncm93dGgsIHkgPSByZXNwb25zZSksIGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZCwgY29sb3IgPSAiYmx1ZSIpICsKICBnZW9tX3BvaW50KGFlcyh4ID0gcG9wdWxhdGlvbl9ncm93dGgsIHkgPSByZXNwb25zZSksIGVpdGhlcl9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gImdyZWVuIikgKwogIGdlb21fcG9pbnQoYWVzKHggPSBwb3B1bGF0aW9uX2dyb3d0aCwgeSA9IHJlc3BvbnNlKSwgYm90aF9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gInB1cnBsZSIpCmBgYApgYGB7cn0KZ2dwbG90KCkgKyAKICBnZW9tX3BvaW50KGFlcyh4ID0gbWVybGluX3Bvb2xfc2l6ZSwgeSA9IHJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkpLCBtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJyZWQiKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IGJpcmRsaWZlX3Bvb2xfc2l6ZSwgeSA9IHJlZ2lvbl8yMGttX2F2ZXJhZ2VfcG9wX2RlbnNpdHkpLCBiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gImJsdWUiKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IGVpdGhlcl9wb29sX3NpemUsIHkgPSByZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5KSwgZWl0aGVyX2NpdHlfZGF0YV9maXhlZCwgY29sb3IgPSAiZ3JlZW4iKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IGJvdGhfcG9vbF9zaXplLCB5ID0gcmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSksIGJvdGhfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJwdXJwbGUiKQpgYGAKCmBgYHtyfQpzdW1tYXJ5KGxtKHJlc3BvbnNlIH4gcG9wdWxhdGlvbl9ncm93dGgsIG1lcmxpbl9jaXR5X2RhdGFfZml4ZWQpKQpzdW1tYXJ5KGxtKHJlc3BvbnNlIH4gcG9wdWxhdGlvbl9ncm93dGgsIGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZCkpCnN1bW1hcnkobG0ocmVzcG9uc2UgfiBwb3B1bGF0aW9uX2dyb3d0aCwgZWl0aGVyX2NpdHlfZGF0YV9maXhlZCkpCnN1bW1hcnkobG0ocmVzcG9uc2UgfiBwb3B1bGF0aW9uX2dyb3d0aCwgYm90aF9jaXR5X2RhdGFfZml4ZWQpKQpgYGAKCmBgYHtyfQpzdW1tYXJ5KGxtKHJlc3BvbnNlIH4gcmFpbmZhbGxfbW9udGhseV9taW4sIG1lcmxpbl9jaXR5X2RhdGFfZml4ZWQpKQpzdW1tYXJ5KGxtKHJlc3BvbnNlIH4gcmFpbmZhbGxfbW9udGhseV9taW4sIGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZCkpCnN1bW1hcnkobG0ocmVzcG9uc2UgfiByYWluZmFsbF9tb250aGx5X21pbiwgZWl0aGVyX2NpdHlfZGF0YV9maXhlZCkpCnN1bW1hcnkobG0ocmVzcG9uc2UgfiByYWluZmFsbF9tb250aGx5X21pbiwgYm90aF9jaXR5X2RhdGFfZml4ZWQpKQpgYGAKYGBge3J9CmdncGxvdCgpICsgCiAgZ2VvbV9wb2ludChhZXMoeCA9IHJhaW5mYWxsX21vbnRobHlfbWluLCB5ID0gcmVzcG9uc2UpLCBtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJyZWQiKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IHJhaW5mYWxsX21vbnRobHlfbWluLCB5ID0gcmVzcG9uc2UpLCBiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gImJsdWUiKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IHJhaW5mYWxsX21vbnRobHlfbWluLCB5ID0gcmVzcG9uc2UpLCBlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJncmVlbiIpICsKICBnZW9tX3BvaW50KGFlcyh4ID0gcmFpbmZhbGxfbW9udGhseV9taW4sIHkgPSByZXNwb25zZSksIGJvdGhfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJwdXJwbGUiKQpgYGAKYGBge3J9CmdncGxvdCgpICsgCiAgZ2VvbV9wb2ludChhZXMoeCA9IHRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlLCB5ID0gcmVzcG9uc2UpLCBtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJyZWQiKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IHRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlLCB5ID0gcmVzcG9uc2UpLCBiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gImJsdWUiKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IHRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlLCB5ID0gcmVzcG9uc2UpLCBlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJncmVlbiIpICsKICBnZW9tX3BvaW50KGFlcyh4ID0gdGVtcGVyYXR1cmVfYW5udWFsX2F2ZXJhZ2UsIHkgPSByZXNwb25zZSksIGJvdGhfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJwdXJwbGUiKQpgYGAKCgpgYGB7cn0KZ2dwbG90KCkgKyAKICBnZW9tX3BvaW50KGFlcyh4ID0gcGVyY2VudGFnZV91cmJhbl9hcmVhX2FzX29wZW5fcHVibGljX3NwYWNlcywgeSA9IHJlc3BvbnNlKSwgbWVybGluX2NpdHlfZGF0YV9maXhlZCwgY29sb3IgPSAicmVkIikgKwogIGdlb21fcG9pbnQoYWVzKHggPSBwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzLCB5ID0gcmVzcG9uc2UpLCBiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gImJsdWUiKSArCiAgZ2VvbV9wb2ludChhZXMoeCA9IHBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMsIHkgPSByZXNwb25zZSksIGVpdGhlcl9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gImdyZWVuIikgKwogIGdlb21fcG9pbnQoYWVzKHggPSBwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzLCB5ID0gcmVzcG9uc2UpLCBib3RoX2NpdHlfZGF0YV9maXhlZCwgY29sb3IgPSAicHVycGxlIikKYGBgCgoKYGBge3J9CmdncGxvdCgpICsgCiAgZ2VvbV9wb2ludChhZXMoeCA9IGhhcHBpbmVzc19uZWdhdGl2ZV9lZmZlY3QsIHkgPSByZXNwb25zZSksIG1lcmxpbl9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gInJlZCIpICsKICBnZW9tX3BvaW50KGFlcyh4ID0gaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCwgeSA9IHJlc3BvbnNlKSwgYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJibHVlIikgKwogIGdlb21fcG9pbnQoYWVzKHggPSBoYXBwaW5lc3NfbmVnYXRpdmVfZWZmZWN0LCB5ID0gcmVzcG9uc2UpLCBlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkLCBjb2xvciA9ICJncmVlbiIpICsKICBnZW9tX3BvaW50KGFlcyh4ID0gaGFwcGluZXNzX25lZ2F0aXZlX2VmZmVjdCwgeSA9IHJlc3BvbnNlKSwgYm90aF9jaXR5X2RhdGFfZml4ZWQsIGNvbG9yID0gInB1cnBsZSIpCmBgYAoKYGBge3J9CmdncGxvdCgpICsgCiAgZ2VvbV9ib3hwbG90KGFlcyh4ID0gcmVzcG9uc2UsIHkgPSBiaW9tZV9uYW1lKSwgbWVybGluX2NpdHlfZGF0YV9maXhlZCkKYGBgCmBgYHtyfQpnZ3Bsb3QoKSArIAogIGdlb21fYm94cGxvdChhZXMoeCA9IHJlc3BvbnNlLCB5ID0gYmlvbWVfbmFtZSksIGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZCkKYGBgCgpgYGB7cn0KZ2dwbG90KCkgKyAKICBnZW9tX2JveHBsb3QoYWVzKHggPSByZXNwb25zZSwgeSA9IGJpb21lX25hbWUpLCBlaXRoZXJfY2l0eV9kYXRhX2ZpeGVkKQpgYGAKCmBgYHtyfQpnZ3Bsb3QoKSArIAogIGdlb21fYm94cGxvdChhZXMoeCA9IHJlc3BvbnNlLCB5ID0gYmlvbWVfbmFtZSksIGJvdGhfY2l0eV9kYXRhX2ZpeGVkKQpgYGAKCmBgYHtyfQpzdW1tYXJ5KGxtKHJlc3BvbnNlIH4gYmlvbWVfbmFtZSwgbWVybGluX2NpdHlfZGF0YV9maXhlZCkpCmBgYAoKCi0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tCkluIFN1bW1hcnkKLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0KUmVzcG9uc2UgaXMgcmVsYXRlZCB0byBudW1iZXIgb2Ygc3BlY2llcyBpbiByZWdpb25hbCBwb29sLCB0aGUgbW9yZSBzcGVjaWVzLCB0aGUgbGVzcyB0aGUgcGVyY2VudGFnZSBvZiBzcGVjaWVzIGluIHRoZSBjaXR5LiBJbmRpY2F0aW5nIGEgZml4ZWQgbnVtYmVyIG9mIHNwZWNpZXMgYXJlIGFibGUgdG8gbW92ZSBpbnRvIGNpdGllcy4KVGhlIHNpemUgb2YgdGhlIHJlZ2lvbmFsIHBvb2wgaXMgY29ycmVsYXRlZCB3aXRoIGJvdGggdGhlIGFtb3VudCBvZiB1cmJhbiBhbmQgY3VsdGl2YXRlZCBsYW5kIGNvdmVyLCBib3RoIHJlZHVjZSBzcGVjaWVzIGluIHRoZSByZWdpb25hbCBwb29sLgoKUmVzcG9uc2UgaXMgYWxzbyBsb3dlciBpbiB3ZXQgYmlvbWVzIGFuZCBhcmVhcyBvZiB0aGUgd29ybGQsIHRoaXMgaXMgc2VlbiB0aHJvdWdoIHRoZSBoaWdoZXIgcmFpbmZhbGwgaW4gdGhlIG1vbnRoIHdpdGggbGVhc3QgcmFpbmZhbGwgaW4gdGhlIHllYXIsIGFuZCBsb3dlciBwZXJjZW50YWdlcyBpbiB3ZXR0ZXIgYmlvbWVzIHN1Y2ggYXMgZmxvb2RlZCBncmFzc2xhbmRzIGFuZCBtb2lzdCBicm9hZGxlYWYgZm9yZXN0cy4KCkZpbmFsbHkgY2l0aWVzIHdpdGggYSBoaWdoZXIgcHJvcG9ydGlvbiBvZiBncmVlbiBwdWJsaWMgc3BhY2UgYXJlIGxlc3MgbGlrZWx5IHRvIGhhdmUgYSBsb3cgcmVzcG9uc2UuCgpgYGB7cn0KbWVybGluX2NpdHlfZGF0YV8yIDwtIGZldGNoX2NpdHlfZGF0YV9mb3IoJ21lcmxpbicsIFQpCmJpcmRsaWZlX2NpdHlfZGF0YV8yIDwtIGZldGNoX2NpdHlfZGF0YV9mb3IoJ2JpcmRsaWZlJywgVCkKYGBgCgpgYGB7cn0KbWVybGluX2J5X3Bvb2xfc2l6ZS5sbSA8LSBsbShyZXNwb25zZSB+IG1lcmxpbl9wb29sX3NpemUsIG1lcmxpbl9jaXR5X2RhdGEpCm1lcmxpbl9jaXR5X2RhdGEkcmVzaWR1YWxzX3Bvb2xfc2l6ZSA8LSByZXNpZChtZXJsaW5fYnlfcG9vbF9zaXplLmxtKQoKYmlyZGxpZmVfYnlfcG9vbF9zaXplLmxtIDwtIGxtKHJlc3BvbnNlIH4gYmlyZGxpZmVfcG9vbF9zaXplLCBiaXJkbGlmZV9jaXR5X2RhdGEpCmJpcmRsaWZlX2NpdHlfZGF0YSRyZXNpZHVhbHNfcG9vbF9zaXplIDwtIHJlc2lkKGJpcmRsaWZlX2J5X3Bvb2xfc2l6ZS5sbSkKYGBgCgpgYGB7cn0KbWVybGluX2J5X3NlbGVjdGVkLmxtIDwtIGxtKHJlc3BvbnNlIH4gbWVybGluX3Bvb2xfc2l6ZSArIHJlYWxtLCBtZXJsaW5fY2l0eV9kYXRhKQptZXJsaW5fY2l0eV9kYXRhJHJlc2lkdWFsc19zZWxlY3RlZCA8LSByZXNpZChtZXJsaW5fYnlfc2VsZWN0ZWQubG0pCgpiaXJkbGlmZV9ieV9zZWxlY3RlZC5sbSA8LSBsbShyZXNwb25zZSB+IGJpcmRsaWZlX3Bvb2xfc2l6ZSArIHBvcHVsYXRpb25fZ3Jvd3RoICsgcmVnaW9uXzEwMGttX2N1bHRpdmF0ZWQgKyBwZXJjZW50YWdlX3VyYmFuX2FyZWFfYXNfb3Blbl9wdWJsaWNfc3BhY2VzICsgYmlvbWVfbmFtZSArIHJhaW5mYWxsX21vbnRobHlfbWluICsgcmVnaW9uXzIwa21fYXZlcmFnZV9wb3BfZGVuc2l0eSArIHBlcm1hbmVudF93YXRlciwgYmlyZGxpZmVfY2l0eV9kYXRhX2ZpeGVkKQpiaXJkbGlmZV9jaXR5X2RhdGEkcmVzaWR1YWxzX3NlbGVjdGVkIDwtIHJlc2lkKGJpcmRsaWZlX2J5X3NlbGVjdGVkLmxtKQpgYGAKCmBgYHtyfQptZXJsaW5fYnlfYWxsLmxtIDwtIGxtKHJlc3BvbnNlIH4gbWVybGluX3Bvb2xfc2l6ZSArIHJlYWxtICsgcG9wdWxhdGlvbl9ncm93dGggKyByZWdpb25fMTAwa21fY3VsdGl2YXRlZCArIHBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMgKyBiaW9tZV9uYW1lICsgcmFpbmZhbGxfbW9udGhseV9taW4gKyByZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5KyBwZXJtYW5lbnRfd2F0ZXIgKyBwb3B1bGF0aW9uX2dyb3d0aCArIHRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlICsgdGVtcGVyYXR1cmVfbW9udGhseV9taW4sIGRhdGEgPSBtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkKQptZXJsaW5fY2l0eV9kYXRhJHJlc2lkdWFsc19hbGwgPC0gcmVzaWQobWVybGluX2J5X2FsbC5sbSkKCmJpcmRsaWZlX2J5X2FsbC5sbSA8LSBsbShyZXNwb25zZSB+IGJpcmRsaWZlX3Bvb2xfc2l6ZSArIHJlYWxtICsgcG9wdWxhdGlvbl9ncm93dGggKyByZWdpb25fMTAwa21fY3VsdGl2YXRlZCArIHBlcmNlbnRhZ2VfdXJiYW5fYXJlYV9hc19vcGVuX3B1YmxpY19zcGFjZXMgKyBiaW9tZV9uYW1lICsgcmFpbmZhbGxfbW9udGhseV9taW4gKyByZWdpb25fMjBrbV9hdmVyYWdlX3BvcF9kZW5zaXR5KyBwZXJtYW5lbnRfd2F0ZXIgKyBwb3B1bGF0aW9uX2dyb3d0aCArIHRlbXBlcmF0dXJlX2FubnVhbF9hdmVyYWdlICsgdGVtcGVyYXR1cmVfbW9udGhseV9taW4sIGRhdGEgPSBiaXJkbGlmZV9jaXR5X2RhdGFfZml4ZWQpCmJpcmRsaWZlX2NpdHlfZGF0YSRyZXNpZHVhbHNfYWxsIDwtIHJlc2lkKGJpcmRsaWZlX2J5X2FsbC5sbSkKYGBgCgpgYGB7cn0KbWVybGluX2J5X3ByZWZlcnJlZC5sbSA8LSBsbShyZXNwb25zZSB+IG1lcmxpbl9wb29sX3NpemUgKyBiaW9tZV9uYW1lICsgcmFpbmZhbGxfbW9udGhseV9taW4sIGRhdGEgPSBtZXJsaW5fY2l0eV9kYXRhX2ZpeGVkKQptZXJsaW5fY2l0eV9kYXRhJHJlc2lkdWFsc19wcmVmZXJyZWQgPC0gcmVzaWQobWVybGluX2J5X3ByZWZlcnJlZC5sbSkKCmJpcmRsaWZlX2J5X3ByZWZlcnJlZC5sbSA8LSBsbShyZXNwb25zZSB+IGJpcmRsaWZlX3Bvb2xfc2l6ZSArIGJpb21lX25hbWUgKyByYWluZmFsbF9tb250aGx5X21pbiwgZGF0YSA9IGJpcmRsaWZlX2NpdHlfZGF0YV9maXhlZCkKYmlyZGxpZmVfY2l0eV9kYXRhJHJlc2lkdWFsc19wcmVmZXJyZWQgPC0gcmVzaWQoYmlyZGxpZmVfYnlfcHJlZmVycmVkLmxtKQpgYGAKCmBgYHtyfQpvcmRlcmVkX2NpdGllcyA8LSBkYXRhLmZyYW1lKAogIHJhbmtlZF9wZXJmb3JtYW5jZSA9IDE6bnJvdyhtZXJsaW5fY2l0eV9kYXRhXzIpLAogIG1lcmxpbl9iYXNlX3Jlc3BvbnNlID0gbWVybGluX2NpdHlfZGF0YV8yJG5hbWVbb3JkZXIoLW1lcmxpbl9jaXR5X2RhdGEkcmVzcG9uc2UpXSwKICBiaXJkbGlmZV9iYXNlX3Jlc3BvbnNlID0gYmlyZGxpZmVfY2l0eV9kYXRhXzIkbmFtZVtvcmRlcigtYmlyZGxpZmVfY2l0eV9kYXRhJHJlc3BvbnNlKV0sCiAgbWVybGluX21vZGVsX3Bvb2xfc2l6ZV9yZXNpZHVhbHMgPSBtZXJsaW5fY2l0eV9kYXRhXzIkbmFtZVtvcmRlcigtbWVybGluX2NpdHlfZGF0YSRyZXNpZHVhbHNfcG9vbF9zaXplKV0sCiAgYmlyZGxpZmVfbW9kZWxfcG9vbF9zaXplX3Jlc2lkdWFscyA9IGJpcmRsaWZlX2NpdHlfZGF0YV8yJG5hbWVbb3JkZXIoLWJpcmRsaWZlX2NpdHlfZGF0YSRyZXNpZHVhbHNfcG9vbF9zaXplKV0sCiAgbWVybGluX21vZGVsX3NlbGVjdGVkX3Jlc2lkdWFscyA9IG1lcmxpbl9jaXR5X2RhdGFfMiRuYW1lW29yZGVyKC1tZXJsaW5fY2l0eV9kYXRhJHJlc2lkdWFsc19zZWxlY3RlZCldLAogIGJpcmRsaWZlX21vZGVsX3NlbGVjdGVkX3Jlc2lkdWFscyA9IGJpcmRsaWZlX2NpdHlfZGF0YV8yJG5hbWVbb3JkZXIoLWJpcmRsaWZlX2NpdHlfZGF0YSRyZXNpZHVhbHNfc2VsZWN0ZWQpXSwKICBtZXJsaW5fbW9kZWxfYWxsX3Jlc2lkdWFscyA9IG1lcmxpbl9jaXR5X2RhdGFfMiRuYW1lW29yZGVyKC1tZXJsaW5fY2l0eV9kYXRhJHJlc2lkdWFsc19hbGwpXSwKICBiaXJkbGlmZV9tb2RlbF9hbGxfcmVzaWR1YWxzID0gYmlyZGxpZmVfY2l0eV9kYXRhXzIkbmFtZVtvcmRlcigtYmlyZGxpZmVfY2l0eV9kYXRhJHJlc2lkdWFsc19hbGwpXSwKICBtZXJsaW5fbW9kZWxfcHJlZmVycmVkX3Jlc2lkdWFscyA9IG1lcmxpbl9jaXR5X2RhdGFfMiRuYW1lW29yZGVyKC1tZXJsaW5fY2l0eV9kYXRhJHJlc2lkdWFsc19wcmVmZXJyZWQpXSwKICBiaXJkbGlmZV9tb2RlbF9wcmVmZXJyZWRfcmVzaWR1YWxzID0gYmlyZGxpZmVfY2l0eV9kYXRhXzIkbmFtZVtvcmRlcigtYmlyZGxpZmVfY2l0eV9kYXRhJHJlc2lkdWFsc19wcmVmZXJyZWQpXQopCm9yZGVyZWRfY2l0aWVzCmBgYAoKYGBge3J9CndyaXRlX2NzdihvcmRlcmVkX2NpdGllcywgImNpdHlfZWZmZWN0X3Jlc2lkdWFscy5jc3YiKQpgYGAKCmBgYHtyfQpsaWJyYXJ5KGdncmVwZWwpCmBgYAoKYGBge3J9Cm1lcmxpbl9jaXR5X2RhdGEkbmFtZSA8LSBtZXJsaW5fY2l0eV9kYXRhXzIkbmFtZQpwbG90X21lcmxpbl9wb29sc2l6ZSA8LSBnZ3Bsb3QobWVybGluX2NpdHlfZGF0YSwgYWVzKHkgPSByZXNwb25zZSwgeCA9IG1lcmxpbl9wb29sX3NpemUpKSArIAogIGdlb21fc21vb3RoKG1ldGhvZCA9ICJsbSIsIHNlID0gRikgKyAKICBnZW9tX3BvaW50KGFlcyhjb2xvciA9IHJlc2lkdWFsc19wb29sX3NpemUpLCBzaXplID0gNCkgKyAKICBnZW9tX2xhYmVsX3JlcGVsKGFlcyhsYWJlbCA9IG5hbWUpLCBzaXplID0gNCkgKwogIHhsYWIoIlBvb2wgU2l6ZSIpICsgeWxhYigiQ2l0eSAgUmFuZG9tIEVmZmVjdCBSZXNwb25zZSIpICsKICBndWlkZXMoY29sb3I9Z3VpZGVfbGVnZW5kKHRpdGxlPSJNb2RlbCByZXNpZHVhbHMgJ3Jlc3BvbnNlIH4gcG9vbF9zaXplJyIpKSArCiAgdGhlbWVfYncoKSArIHRoZW1lKGxlZ2VuZC5wb3NpdGlvbj0iYm90dG9tIiwgbGVnZW5kLnRpdGxlPWVsZW1lbnRfdGV4dChzaXplPTkpLCBsZWdlbmQudGV4dD1lbGVtZW50X3RleHQoc2l6ZT04KSwgbGVnZW5kLmtleS5zaXplID0gdW5pdCgxLCJsaW5lIikpICsKICBsYWJzKHRpdGxlID0gIk1lcmxpbiByZXNwb25zZSBnaXZlbiBwb29sIHNpemUiKQpwbG90X21lcmxpbl9wb29sc2l6ZQpgYGAKCmBgYHtyfQpiaXJkbGlmZV9jaXR5X2RhdGEkbmFtZSA8LSBiaXJkbGlmZV9jaXR5X2RhdGFfMiRuYW1lCnBsb3RfYmlyZGxpZmVfcG9vbHNpemUgPC0gZ2dwbG90KGJpcmRsaWZlX2NpdHlfZGF0YSwgYWVzKHkgPSByZXNwb25zZSwgeCA9IGJpcmRsaWZlX3Bvb2xfc2l6ZSkpICsgCiAgZ2VvbV9zbW9vdGgobWV0aG9kID0gImxtIiwgc2UgPSBGKSArIAogIGdlb21fcG9pbnQoYWVzKGNvbG9yID0gcmVzaWR1YWxzX3Bvb2xfc2l6ZSksIHNpemUgPSA0KSArIAogIGdlb21fbGFiZWxfcmVwZWwoYWVzKGxhYmVsID0gbmFtZSksIHNpemUgPSA0KSArCiAgeGxhYigiUG9vbCBTaXplIikgKyB5bGFiKCJDaXR5ICBSYW5kb20gRWZmZWN0IFJlc3BvbnNlIikgKwogIGd1aWRlcyhjb2xvcj1ndWlkZV9sZWdlbmQodGl0bGU9Ik1vZGVsIHJlc2lkdWFscyAncmVzcG9uc2UgfiBwb29sX3NpemUnIikpICsKICB0aGVtZV9idygpICsgdGhlbWUobGVnZW5kLnBvc2l0aW9uPSJib3R0b20iLCBsZWdlbmQudGl0bGU9ZWxlbWVudF90ZXh0KHNpemU9OSksIGxlZ2VuZC50ZXh0PWVsZW1lbnRfdGV4dChzaXplPTgpLCBsZWdlbmQua2V5LnNpemUgPSB1bml0KDEsImxpbmUiKSkgKwogIGxhYnModGl0bGUgPSAiQmlyZGxpZmUgcmVzcG9uc2UgZ2l2ZW4gcG9vbCBzaXplIikKcGxvdF9iaXJkbGlmZV9wb29sc2l6ZQpgYGAKCmBgYHtyfQpwbG90X21lcmxpbl9wcmVmZXJyZWQgPC0gZ2dwbG90KG1lcmxpbl9jaXR5X2RhdGEsIGFlcyh5ID0gcmVzcG9uc2UsIHggPSBtZXJsaW5fcG9vbF9zaXplKSkgKyAKICBnZW9tX3Ntb290aChtZXRob2QgPSAibG0iLCBzZSA9IEYpICsgCiAgZ2VvbV9wb2ludChhZXMoY29sb3IgPSByZXNpZHVhbHNfcHJlZmVycmVkKSwgc2l6ZSA9IDQpICsgCiAgZ2VvbV9sYWJlbF9yZXBlbChhZXMobGFiZWwgPSBuYW1lKSwgc2l6ZSA9IDQpICsKICB4bGFiKCJQb29sIFNpemUiKSArIHlsYWIoIkNpdHkgIFJhbmRvbSBFZmZlY3QgUmVzcG9uc2UiKSArCiAgZ3VpZGVzKGNvbG9yPWd1aWRlX2xlZ2VuZCh0aXRsZT0iTW9kZWwgcmVzaWR1YWxzICdyZXNwb25zZSB+IHBvb2xfc2l6ZSArIGJpb21lICsgcmFpbmZhbGxfbWluJyIpKSArCiAgdGhlbWVfYncoKSArIHRoZW1lKGxlZ2VuZC5wb3NpdGlvbj0iYm90dG9tIiwgbGVnZW5kLnRpdGxlPWVsZW1lbnRfdGV4dChzaXplPTkpLCBsZWdlbmQudGV4dD1lbGVtZW50X3RleHQoc2l6ZT04KSwgbGVnZW5kLmtleS5zaXplID0gdW5pdCgxLCJsaW5lIikpICsKICBsYWJzKHRpdGxlID0gIk1lcmxpbiByZXNwb25zZSBnaXZlbiBwb29sIHNpemUsIGJpb21lLCBhbmQgcmFpbmZhbGwiKQpwbG90X21lcmxpbl9wcmVmZXJyZWQKYGBgCgpgYGB7cn0KcGxvdF9iaXJkbGlmZV9wcmVmZXJyZWQgPC0gZ2dwbG90KGJpcmRsaWZlX2NpdHlfZGF0YSwgYWVzKHkgPSByZXNwb25zZSwgeCA9IGJpcmRsaWZlX3Bvb2xfc2l6ZSkpICsgCiAgZ2VvbV9zbW9vdGgobWV0aG9kID0gImxtIiwgc2UgPSBGKSArIAogIGdlb21fcG9pbnQoYWVzKGNvbG9yID0gcmVzaWR1YWxzX3ByZWZlcnJlZCksIHNpemUgPSA0KSArIAogIGdlb21fbGFiZWxfcmVwZWwoYWVzKGxhYmVsID0gbmFtZSksIHNpemUgPSA0KSArCiAgeGxhYigiUG9vbCBTaXplIikgKyB5bGFiKCJDaXR5ICBSYW5kb20gRWZmZWN0IFJlc3BvbnNlIikgKwogIGd1aWRlcyhjb2xvcj1ndWlkZV9sZWdlbmQodGl0bGU9Ik1vZGVsIHJlc2lkdWFscyAncmVzcG9uc2UgfiBwb29sX3NpemUgKyBiaW9tZSArIHJhaW5mYWxsX21pbiciKSkgKwogIHRoZW1lX2J3KCkgKyB0aGVtZShsZWdlbmQucG9zaXRpb249ImJvdHRvbSIsIGxlZ2VuZC50aXRsZT1lbGVtZW50X3RleHQoc2l6ZT05KSwgbGVnZW5kLnRleHQ9ZWxlbWVudF90ZXh0KHNpemU9OCksIGxlZ2VuZC5rZXkuc2l6ZSA9IHVuaXQoMSwibGluZSIpKSArCiAgbGFicyh0aXRsZSA9ICJCaXJkbGlmZSByZXNwb25zZSBnaXZlbiBwb29sIHNpemUsIGJpb21lLCBhbmQgcmFpbmZhbGwiKQpwbG90X2JpcmRsaWZlX3ByZWZlcnJlZApgYGAKCmBgYHtyfQpsaWJyYXJ5KGdncHVicikKYGBgCgoKYGBge3J9CnBsb3RfcmVzaWR1YWxzIDwtIGdnYXJyYW5nZShwbG90X21lcmxpbl9wb29sc2l6ZSwgcGxvdF9iaXJkbGlmZV9wb29sc2l6ZSwgcGxvdF9tZXJsaW5fcHJlZmVycmVkLCBwbG90X2JpcmRsaWZlX3ByZWZlcnJlZCkKcGxvdF9yZXNpZHVhbHMKYGBgCmBgYHtyfQpqcGVnKCJjaXR5X2VmZmVjdF9yZXNpZHVhbHMuanBnIiwgd2lkdGggPSAxNjAwLCBoZWlnaHQgPSAxMjAwKQpwbG90X3Jlc2lkdWFscwpkZXYub2ZmKCkKYGBgCgpgYGB7cn0KbWVybGluX2NpdHlfZGF0YV8yMDAgPC0gbWVybGluX2NpdHlfZGF0YVttZXJsaW5fY2l0eV9kYXRhJG1lcmxpbl9wb29sX3NpemUgPiAxOTAgJiBtZXJsaW5fY2l0eV9kYXRhJG1lcmxpbl9wb29sX3NpemUgPCAyMTAsXQptZXJsaW5fY2l0eV9kYXRhXzIwMFtvcmRlcihtZXJsaW5fY2l0eV9kYXRhXzIwMCRyZXNwb25zZSksIGMoIm5hbWUiLCAicmVzcG9uc2UiKV0KYGBgCgoKYGBge3J9CmJpcmRsaWZlX2NpdHlfZGF0YV8yMDAgPC0gYmlyZGxpZmVfY2l0eV9kYXRhW2JpcmRsaWZlX2NpdHlfZGF0YSRiaXJkbGlmZV9wb29sX3NpemUgPiAxOTAgJiBiaXJkbGlmZV9jaXR5X2RhdGEkYmlyZGxpZmVfcG9vbF9zaXplIDwgMjEwLF0KYmlyZGxpZmVfY2l0eV9kYXRhXzIwMFtvcmRlcihiaXJkbGlmZV9jaXR5X2RhdGFfMjAwJHJlc3BvbnNlKSwgYygibmFtZSIsICJyZXNwb25zZSIpXQpgYGAKCmBgYHtyfQpiaXJkbGlmZV9jaXR5X2RhdGFfMzAwIDwtIGJpcmRsaWZlX2NpdHlfZGF0YVtiaXJkbGlmZV9jaXR5X2RhdGEkYmlyZGxpZmVfcG9vbF9zaXplID4gMjkwICYgYmlyZGxpZmVfY2l0eV9kYXRhJGJpcmRsaWZlX3Bvb2xfc2l6ZSA8IDMxMCxdCmJpcmRsaWZlX2NpdHlfZGF0YV8zMDBbb3JkZXIoYmlyZGxpZmVfY2l0eV9kYXRhXzMwMCRyZXNwb25zZSksIGMoIm5hbWUiLCAicmVzcG9uc2UiKV0KYGBgCmBgYHtyfQptZXJsaW5fY2l0eV9kYXRhXzMwMCA8LSBtZXJsaW5fY2l0eV9kYXRhW21lcmxpbl9jaXR5X2RhdGEkbWVybGluX3Bvb2xfc2l6ZSA+IDI5MCAmIG1lcmxpbl9jaXR5X2RhdGEkbWVybGluX3Bvb2xfc2l6ZSA8IDMxMCxdCm1lcmxpbl9jaXR5X2RhdGFfMzAwW29yZGVyKG1lcmxpbl9jaXR5X2RhdGFfMzAwJHJlc3BvbnNlKSwgYygibmFtZSIsICJyZXNwb25zZSIpXQpgYGAKCmBgYHtyfQptZXJsaW5fY2l0eV9kYXRhXzIwMCRsYWJlbCA9ICIyMDAgKE1lcmxpbikiCm1lcmxpbl9jaXR5X2RhdGFfMjAwJHBvb2xfc2l6ZSA9IG1lcmxpbl9jaXR5X2RhdGFfMjAwJG1lcmxpbl9wb29sX3NpemUKbWVybGluX2NpdHlfZGF0YV8zMDAkbGFiZWwgPSAiMzAwIChNZXJsaW4pIgptZXJsaW5fY2l0eV9kYXRhXzMwMCRwb29sX3NpemUgPSBtZXJsaW5fY2l0eV9kYXRhXzMwMCRtZXJsaW5fcG9vbF9zaXplCmJpcmRsaWZlX2NpdHlfZGF0YV8yMDAkbGFiZWwgPSAiMjAwIChCaXJkbGlmZSkiCmJpcmRsaWZlX2NpdHlfZGF0YV8yMDAkcG9vbF9zaXplID0gYmlyZGxpZmVfY2l0eV9kYXRhXzIwMCRiaXJkbGlmZV9wb29sX3NpemUKYmlyZGxpZmVfY2l0eV9kYXRhXzMwMCRsYWJlbCA9ICIzMDAgKEJpcmRsaWZlKSIKYmlyZGxpZmVfY2l0eV9kYXRhXzMwMCRwb29sX3NpemUgPSBiaXJkbGlmZV9jaXR5X2RhdGFfMzAwJGJpcmRsaWZlX3Bvb2xfc2l6ZQpjaXR5X2RhdGFfc3Vic2V0IDwtIHJiaW5kKG1lcmxpbl9jaXR5X2RhdGFfMjAwWyxjKCJuYW1lIiwgInJlc3BvbnNlIiwgInBvb2xfc2l6ZSIsICJsYWJlbCIsICJiaW9tZV9uYW1lIildLCBtZXJsaW5fY2l0eV9kYXRhXzMwMFssYygibmFtZSIsICJyZXNwb25zZSIsICJwb29sX3NpemUiLCAibGFiZWwiLCAiYmlvbWVfbmFtZSIpXSwgYmlyZGxpZmVfY2l0eV9kYXRhXzIwMFssYygibmFtZSIsICJyZXNwb25zZSIsICJwb29sX3NpemUiLCAibGFiZWwiLCAiYmlvbWVfbmFtZSIpXSwgYmlyZGxpZmVfY2l0eV9kYXRhXzMwMFssYygibmFtZSIsICJyZXNwb25zZSIsICJwb29sX3NpemUiLCAibGFiZWwiLCAiYmlvbWVfbmFtZSIpXSkKCgpnZ3Bsb3QoY2l0eV9kYXRhX3N1YnNldCwgYWVzKHggPSBsYWJlbCwgeSA9IHJlc3BvbnNlLCBjb2xvciA9IGxhYmVsKSkgKyAKICBnZW9tX2xhYmVsX3JlcGVsKGFlcyhsYWJlbCA9IG5hbWUpLCBzaXplID0gMykgKyBnZW9tX3BvaW50KCkgKwogIHRoZW1lX2J3KCkgKyB0aGVtZShsZWdlbmQucG9zaXRpb24gPSAibm9uZSIpICsgeGxhYigiUG9vbCBzaXplIChQb29sKSIpICsgeWxhYigiUmFuZG9tIEVmZmVjdCBSZXNwb25zZSIpCmBgYApgYGB7cn0KZ2dwbG90KGNpdHlfZGF0YV9zdWJzZXQsIGFlcyh4ID0gbGFiZWwsIHkgPSByZXNwb25zZSwgY29sb3IgPSBsYWJlbCkpICsgCiAgZ2VvbV9sYWJlbF9yZXBlbChhZXMobGFiZWwgPSBiaW9tZV9uYW1lKSwgc2l6ZSA9IDMpICsgZ2VvbV9wb2ludCgpICsKICB0aGVtZV9idygpICsgdGhlbWUobGVnZW5kLnBvc2l0aW9uID0gIm5vbmUiKSArIHhsYWIoIlBvb2wgc2l6ZSAoUG9vbCkiKSArIHlsYWIoIlJhbmRvbSBFZmZlY3QgUmVzcG9uc2UiKQpgYGAKYGBge3J9CnRhYmxlKGNpdHlfZGF0YSRiaW9tZV9uYW1lKQpgYGAKCmBgYHtyfQpzdW1tYXJ5KGdsbShyZXNwb25zZSB+IHJlbGV2ZWwoYmlvbWVfbmFtZSwgcmVmID0gIlRlbXBlcmF0ZSBCcm9hZGxlYWYgJiBNaXhlZCBGb3Jlc3RzIikgKyBtZXJsaW5fcG9vbF9zaXplLCBtZXJsaW5fY2l0eV9kYXRhLCBmYW1pbHkgPSAiZ2F1c3NpYW4iKSkKYGBgCgpgYGB7cn0Kc3VtbWFyeShnbG0ocmVzcG9uc2UgfiByZWxldmVsKGJpb21lX25hbWUsIHJlZiA9ICJUZW1wZXJhdGUgQnJvYWRsZWFmICYgTWl4ZWQgRm9yZXN0cyIpICsgYmlyZGxpZmVfcG9vbF9zaXplLCBiaXJkbGlmZV9jaXR5X2RhdGEsIGZhbWlseSA9ICJnYXVzc2lhbiIpKQpgYGAKCmBgYHtyfQp1bmlxdWUoY2l0eV9kYXRhJGJpb21lX25hbWUpCmBgYAoKYGBge3J9CnNwbGl0X2Jpb21lIDwtIGZ1bmN0aW9uKGRhdGFzZXQpIHsKICBkYXRhc2V0JGJpb21lX3ZlZ2V0YXRpb24gPC0gJ0ZvcmVzdHMnCiAgZGF0YXNldCRiaW9tZV92ZWdldGF0aW9uW2RhdGFzZXQkYmlvbWVfbmFtZSA9PSAnVHJvcGljYWwgJiBTdWJ0cm9waWNhbCBHcmFzc2xhbmRzLCBTYXZhbm5hcyAmIFNocnVibGFuZHMnXSA8LSAnR3Jhc3NsYW5kICYgU2hydWJsYW5kcycKICBkYXRhc2V0JGJpb21lX3ZlZ2V0YXRpb25bZGF0YXNldCRiaW9tZV9uYW1lID09ICdNb250YW5lIEdyYXNzbGFuZHMgJiBTaHJ1YmxhbmRzJ10gPC0gJ0dyYXNzbGFuZCAmIFNocnVibGFuZHMnCiAgZGF0YXNldCRiaW9tZV92ZWdldGF0aW9uW2RhdGFzZXQkYmlvbWVfbmFtZSA9PSAnVGVtcGVyYXRlIEdyYXNzbGFuZHMsIFNhdmFubmFzICYgU2hydWJsYW5kcyddIDwtICdHcmFzc2xhbmQgJiBTaHJ1YmxhbmRzJwogIGRhdGFzZXQkYmlvbWVfdmVnZXRhdGlvbltkYXRhc2V0JGJpb21lX25hbWUgPT0gJ0Zsb29kZWQgR3Jhc3NsYW5kcyAmIFNhdmFubmFzJ10gPC0gJ0dyYXNzbGFuZCAmIFNocnVibGFuZHMnCiAgZGF0YXNldCRiaW9tZV92ZWdldGF0aW9uW2RhdGFzZXQkYmlvbWVfbmFtZSA9PSAnRGVzZXJ0cyAmIFhlcmljIFNocnVibGFuZHMnXSA8LSAnR3Jhc3NsYW5kICYgU2hydWJsYW5kcycKICAKICBkYXRhc2V0JGJpb21lX3ZlZ2V0YXRpb24gPC0gYXMuZmFjdG9yKGRhdGFzZXQkYmlvbWVfdmVnZXRhdGlvbikKICAKICBkYXRhc2V0JGJpb21lX2xvY2F0aW9uIDwtICdUcm9waWNhbCAmIFN1YnRyb3BpY2FsJwogIGRhdGFzZXQkYmlvbWVfbG9jYXRpb25bZGF0YXNldCRiaW9tZV9uYW1lID09ICdNb250YW5lIEdyYXNzbGFuZHMgJiBTaHJ1YmxhbmRzJ10gPC0gJ01vbnRhbmUnCiAgZGF0YXNldCRiaW9tZV9sb2NhdGlvbltkYXRhc2V0JGJpb21lX25hbWUgPT0gJ01lZGl0ZXJyYW5lYW4gRm9yZXN0cywgV29vZGxhbmRzICYgU2NydWInXSA8LSAnTWVkaXRlcnJhbmVhbicKICBkYXRhc2V0JGJpb21lX2xvY2F0aW9uW2RhdGFzZXQkYmlvbWVfbmFtZSA9PSAnVGVtcGVyYXRlIEJyb2FkbGVhZiAmIE1peGVkIEZvcmVzdHMnXSA8LSAnVGVtcGVyYXRlJwogIGRhdGFzZXQkYmlvbWVfbG9jYXRpb25bZGF0YXNldCRiaW9tZV9uYW1lID09ICdUZW1wZXJhdGUgR3Jhc3NsYW5kcywgU2F2YW5uYXMgJiBTaHJ1YmxhbmRzJ10gPC0gJ1RlbXBlcmF0ZScKICBkYXRhc2V0JGJpb21lX2xvY2F0aW9uW2RhdGFzZXQkYmlvbWVfbmFtZSA9PSAnVGVtcGVyYXRlIENvbmlmZXIgRm9yZXN0cyddIDwtICdUZW1wZXJhdGUnCiAgZGF0YXNldCRiaW9tZV9sb2NhdGlvbltkYXRhc2V0JGJpb21lX25hbWUgPT0gJ0Rlc2VydHMgJiBYZXJpYyBTaHJ1YmxhbmRzJ10gPC0gJ0Rlc2VydCcKICBkYXRhc2V0JGJpb21lX2xvY2F0aW9uW2RhdGFzZXQkYmlvbWVfbmFtZSA9PSAnQm9yZWFsIEZvcmVzdHMvVGFpZ2EnXSA8LSAnQm9yZWFsJwogIGRhdGFzZXQkYmlvbWVfbG9jYXRpb25bZGF0YXNldCRiaW9tZV9uYW1lID09ICdGbG9vZGVkIEdyYXNzbGFuZHMgJiBTYXZhbm5hcyddIDwtICdHbG9iYWwnCiAgCiAgZGF0YXNldCRiaW9tZV9sb2NhdGlvbiA8LSBhcy5mYWN0b3IoZGF0YXNldCRiaW9tZV9sb2NhdGlvbikKICAKICBkYXRhc2V0JGJpb21lX2NsaW1hdGUgPC0gJ05vcm1hbCcKICBkYXRhc2V0JGJpb21lX2NsaW1hdGVbZGF0YXNldCRiaW9tZV9uYW1lID09ICdUcm9waWNhbCAmIFN1YnRyb3BpY2FsIE1vaXN0IEJyb2FkbGVhZiBGb3Jlc3RzJ10gPC0gJ1dldCcKICBkYXRhc2V0JGJpb21lX2NsaW1hdGVbZGF0YXNldCRiaW9tZV9uYW1lID09ICdGbG9vZGVkIEdyYXNzbGFuZHMgJiBTYXZhbm5hcyddIDwtICdXZXQnCiAgZGF0YXNldCRiaW9tZV9jbGltYXRlW2RhdGFzZXQkYmlvbWVfbmFtZSA9PSAnVHJvcGljYWwgJiBTdWJ0cm9waWNhbCBEcnkgQnJvYWRsZWFmIEZvcmVzdHMnXSA8LSAnRHJ5JwogIGRhdGFzZXQkYmlvbWVfY2xpbWF0ZVtkYXRhc2V0JGJpb21lX25hbWUgPT0gJ0Rlc2VydHMgJiBYZXJpYyBTaHJ1YmxhbmRzJ10gPC0gJ0RyeScKICAKICBkYXRhc2V0JGJpb21lX2NsaW1hdGUgPC0gYXMuZmFjdG9yKGRhdGFzZXQkYmlvbWVfY2xpbWF0ZSkKICAKICBkYXRhc2V0Cn0KYGBgCgoKYGBge3J9CnRtcCA8LSBzcGxpdF9iaW9tZShtZXJsaW5fY2l0eV9kYXRhKQpzdW1tYXJ5KGdsbShyZXNwb25zZSB+IGJpb21lX3ZlZ2V0YXRpb24gKyBiaW9tZV9sb2NhdGlvbiArIGJpb21lX2NsaW1hdGUgKyBtZXJsaW5fcG9vbF9zaXplLCB0bXAsIGZhbWlseSA9ICJnYXVzc2lhbiIpKQpgYGAKCmBgYHtyfQpzdW1tYXJ5KGdsbShyZXNwb25zZSB+IGJpb21lX25hbWUgKyByYWluZmFsbF9tb250aGx5X21pbiAqIG1lcmxpbl9wb29sX3NpemUsIHRtcCwgZmFtaWx5ID0gImdhdXNzaWFuIikpCmBgYAoKYGBge3J9CmxpYnJhcnkoYm9vdCkKYGBgCgoKYGBge3J9CnJzcSA8LSBmdW5jdGlvbihmb3JtdWxhLCBkYXRhLCBpbmRpY2VzKSB7CiAgZCA8LSBkYXRhW2luZGljZXMsXSAjIGFsbG93cyBib290IHRvIHNlbGVjdCBzYW1wbGUKICBmaXQgPC0gZ2xtKGZvcm11bGEsIGRhdGE9ZCkKICB3aXRoKHN1bW1hcnkoZml0KSwgMSAtIGRldmlhbmNlL251bGwuZGV2aWFuY2UpCn0gCgpyZXN1bHRzIDwtIGJvb3QoZGF0YT1tZXJsaW5fY2l0eV9kYXRhLCBzdGF0aXN0aWM9cnNxLCBSPTEwMDAsIGZvcm11bGE9cmVzcG9uc2UgfiBiaW9tZV9uYW1lICsgbWVybGluX3Bvb2xfc2l6ZSkKYm9vdC5jaShyZXN1bHRzLCB0eXBlPSJiY2EiKQpyZXN1bHRzCmBgYAoKYGBge3J9CnJlc3VsdHMgPC0gYm9vdChkYXRhPW1lcmxpbl9jaXR5X2RhdGEsIHN0YXRpc3RpYz1yc3EsIFI9MTAwMCwgZm9ybXVsYT1yZXNwb25zZSB+IGJpb21lX25hbWUgKyByYWluZmFsbF9tb250aGx5X21pbiAqIG1lcmxpbl9wb29sX3NpemUpCmJvb3QuY2kocmVzdWx0cywgdHlwZT0iYmNhIikKcmVzdWx0cwpgYGAKCmBgYHtyfQpyZXN1bHRzIDwtIGJvb3QoZGF0YT1tZXJsaW5fY2l0eV9kYXRhLCBzdGF0aXN0aWM9cnNxLCBSPTEwMDAsIGZvcm11bGE9cmVzcG9uc2UgfiBtZXJsaW5fcG9vbF9zaXplKQpib290LmNpKHJlc3VsdHMsIHR5cGU9ImJjYSIpCnJlc3VsdHMKYGBgCgo=